Arabic Twitter Sentiment Toward ISIS and the United States

RESEARCH NOTE—The Enemy of My Enemy Is Not My Friend: Arabic Twitter Sentiment Toward ISIS and the United States

David Romney,1 Amaney Jamal,2 Robert O. Keohane,3 and Dustin Tingley4

This draft: June 6, 20205

Abstract (125 words): A counter-intuitive finding emerges from an analysis of Arabic Twit- ter posts from 2014–2015: Twitter participants who are negative toward the Islamic State of Iraq and the Levant (ISIS) are also more likely to hold negative views of the United States (US). This surprising correlation is due to the interpretations of two sets of users. One set of users views the

US and ISIS negatively as independent interventionist powers in the region. The other set of users negatively links the US with ISIS, often asserting a secretive conspiracy between the two. The intense negativity toward the United States in the Middle East seems conducive to views that, in one way or another, cause citizens to link the United States and ISIS in a conspiratorial manner.

1Harvard University, Department of Government 2Princeton University, Politics Department 3Princeton University, Woodrow Wilson School of Public and International Affairs 4Harvard University, Department of Government Please send questions to our corresponding author at [email protected]. Our thanks to the Crimson Hexagon/Brandwatch team for working with us to gain access to their platform. Thanks to Annabell Barry for research assistance. We also appreciate the excellent comments we received at our panel at ISA 2017 and written comments from Sara Bush, as well as comments at our panel at ISA 2020. 1. Introduction

The United States’ (US) war against the Islamic State of Iraq and the Levant (ISIS) has been waged both on the ground in Iraq and Syria and online in social media (Greenberg 2016). Social media plays an increasingly important role in a range of conflicts (Zeitzoff 2017), and this rising role of social media not only changes the way these conflicts are fought but also how they can be studied.

In the case of ISIS and the US, social media provides us with new opportunities to examine how publics in or adjacent to the conflict form attitudes toward the entities involved. This is particularly interesting in the case of the US and ISIS because Arabs intensely dislike both entities. Only 0.4 to 6.4% across the Arab world agree with ISIS’s goals (Tessler, Robbins, and Jamal, 2016), but the US is also exceedingly unpopular (Jamal et al. 2015; Lynch 2007), with favorable views rang- ing from 15 to 34% (Vice 2017). These two disliked entities have both engaged in activities that impinge heavily on the domestic politics of the countries of the Middle East; however, at the same time, they have been fighting a war against each other. In this scenario, where two disliked entities fight each other, how do citizens pick a side, if they pick one at all? Do their attitudes shift because of a stronger dislike for one of those entities (i.e. an “enemy of my enemy is my friend” dynamic), or do other factors guide their attitudes?

We address this question by examining Arabic Twitter posts about the US and ISIS from

2014 to 2015, building on previous work on politics in social media (Barceló and Labzina 2018;

Jamal et al. 2015; Zeitzoff et al. 2015) and anti-Americanism (Sokolov et al. 2018; Corstange

2016; Katzenstein and Keohane 2007). We find results that are not consistent with the “enemy of my enemy is my friend” interpretation in this context, instead finding that Arab Twitter publics simultaneously dislike the US and ISIS and that expressed dislike of both entities increases as the

US intervenes against ISIS. We provide evidence that two distinct groups of Twitter participants

2 drive the correlation between negative sentiment toward the US and ISIS. One group negatively views the US and ISIS but sees them as acting independently; the other sees them as linked, often in a conspiratorial manner; and what ties these two groups is a strong negative sentiment toward intervening forces in the region.

2. Possible Outcomes and Explanations

This project was initiated as an inductive exercise. For this reason, in this section we outline the outcomes of interest that were possible at the outset of our data gathering exercise and then describe the theories that most likely explain those outcomes. To simplify this section, we avoid discussions of outcomes where sentiment toward the US and ISIS is not correlated as well as discussions of positive sentiment toward ISIS.

Outcome 1: The Enemy of My Enemy Is My Friend

A first possible outcome would be to find that negative sentiment toward ISIS correlates with positive sentiment toward the US. This outcome would be consistent with an “enemy of my enemy is my friend” dynamic, wherein one traditional enemy is supported because of their offer to aid in fighting another enemy. Such a dynamic has been used as a framework to study topics central to international relations, including: states' decisions to intervene in civil wars (Findley and Teo

2006), interstate disputes (Joyce et al. 2013), the likelihood of terrorists attacking nationals of countries which are allied with the terrorists’ home country (Plümper and Neumayer 2009), and flows of international trade (Polachek et al. 1999). Studying this dynamic is important because an

“enemy of my enemy is my friend” logic also reflects a reality of dyadic behavior—that it is de- pendent on attitudes and allegiances toward third parties—that often goes unacknowledged (Cran- mer and Desmarais 2016).

3 Similarly, an “enemy of my enemy is my friend” dynamic may govern the behavior of individuals and public opinion, a factor that is often important in mediating a state's response in the arena of foreign policy (Foyle 1997). In the realm of individual beliefs and attitudes, this first possible outcome of our empirical work would be most in line with a view that public opinion is a function of perceived self-interest on the part of citizens. That is, individuals form opinions based on what they believe will most benefit their interests. Scholars have modeled opinion formation in this way in a variety of contexts. In international trade, it is often posited that public opinion on trade policy is primarily a function of who stands to gain or be hurt by said policies (Mayda and

Rodrik 2005). Some have found that changes in public opinion as it relates to foreign policy are similarly “reasonable,” i.e. they reflects shifts in the real interests of the nation (Shapiro and Page

1988; Isernia et al. 2002). In the context of this study, it could be that the US's engagement against a common foe (ISIS) causes those who express negative sentiment toward ISIS to embrace at least some aspects of US involvement in the fight against ISIS.

Outcome 2: The Enemy of My Enemy Is Not My Friend

A second possible outcome is that negative sentiment toward ISIS is correlated with negative sentiment toward the US. Such an outcome would be consistent with at least two separate interpretations, the first of which we label (1) independent negativity. Under this interpretation, members of the Arabic Twitter universe have a firm, long-term dislike of the US; they believe that US actions are hostile to Arabs. They also perceive ISIS as hostile to their values and perhaps directly threatening to themselves. But, in spite of the US's engagement against ISIS, they do not connect their negativity toward the US with their negative opinions of ISIS; these two entities are viewed as independent of each other. The second we label (2) linked negativity. Under this interpretation, citizens' negativity toward the two entities is connected because they hold the US responsible for

4 ISIS. Additionally, there are two interpretations that would be consistent with citizens holding the

US responsible for ISIS. First, under the (A) non-conspiratorial interpretation, citizens may believe that US actions in the region have inadvertently led to the growth of ISIS. Therefore, they view US actions as integral to allowing for the rise of ISIS but do not purport a direct connection between the two. The second way that citizens may view the US as responsible for ISIS is under what we label the (B) conspiratorial interpretation. Under this interpretation, the US and ISIS are in cahoots, with either the US purposefully creating ISIS or engaging in efforts to support and strengthen its power in the Middle East.

Some of these interpretations, especially (2B), may appear outlandish. However, research on attitude formation across a variety of contexts has demonstrated that accuracy is not always individuals’ goal when adjusting opinions and beliefs. One of the most prominent sets of literature on this topic is research on motivated reasoning, which notes that people often form or update opinions as a way of reaching a preferred conclusion rather than out of a desire to be accurate

(Taber and Lodge 2006). Much of this research has focused on the US, and in that context has found that motivated reasoning takes a partisan hue. For instance, Hellwig et al. (2008) find that citizens evaluate their government's ability to negotiate in international trade disputes based on their partisan identity; similarly, Gaines et al. (2007) find that motivated reasoning functions in citizens' evaluations of foreign policy, in their case the war in Iraq, because citizens interpretations of facts varied by partisan identity. Additionally, motivated reasoning is particularly prevalent when looking at the issues that individuals care about the most. Studies of motivated reasoning have often focused on topics that are controversial or about which individuals are likely to hold strong opinions, for instance stem cell research (Nyhan and Reifler 2010) or gun control and af- firmative action (Taber and Lodge 2006). In studies where the public has less information or is

5 less opinionated, such as in opinions on foreign aid (Hurst et al. 2017), scholars have found individuals less susceptible to motivated reasoning.

Our context, though outside the US, could see similar factors at work because of the Arab

Middle East's historical experience with intervention by the US and other foreign powers, making this a context where the operation of motivational reasoning is plausible. As noted, anti-US sentiment is very strong in the Middle East, and the US is seen consistently seen as one of the most salient and important countries to the foreign policy of Middle Eastern countries (Lynch 2007;

Vice 2017). Negative attitudes toward the US are integrally tied to US intervention in the region

(Jamal et al. 2015)—even if that intervention was against another country. As Furia and Lucas

(2006) note, Arab attitudes toward foreign countries are primarily “based on the military and non- military policy behaviors of those countries in regard to regionally salient issues,” the Iraq war and Palestine in particular. Additionally, experience with real foreign plots in the past adds to the potential to interpret the present in conspiratorial terms. As just one example, secret negotiations between foreign powers (the so-called Sykes-Picot agreement) led to French and British mandates of control over some Middle East states post WWII. Any belief in what seem like outlandish theories about foreign powers cannot be fully understood without the recognition of historical expe- riences like this, many of which are still salient to Arabs across the Middle East to this day (Gray

2010; Silverstein 2000).

Summary

We focus on two possible outcomes of our empirical exploration: one where negativity toward

ISIS is correlated with positivity toward the US (enemy of my enemy is my friend), and one where

ISIS negativity is correlated with a similar negativity toward the US. A confluence of negativity could be happenstance—i.e. individuals' negative views toward ISIS and the US could remain

6 independent of each other, despite US intervention against ISIS. However, an interpretation based on motivational reasoning and on the Arab Middle East's historical experience would predict that individuals draw a connection between ISIS and the US, potentially in a conspiratorial manner. In the sections that follow, we establish that negativity toward ISIS coincides with negativity toward the US in Arabic Twitter, examine alternative explanations for the correlation, and then examine the connections Arabic Twitter users make between ISIS and the US. We conclude that many

Arabic Twitter users who are negative toward ISIS adopt a conspiratorial view of the role of the

United States in the Middle East.

3. Data and Analysis

Our data consist of all public Arabic Twitter posts 2014–2015, which we gain access to and analyze with textual analysis models on the platform of Crimson Hexagon (CH), a social media analytics company. Throughout the rest of the paper, we will refer to several textual analysis models we used to estimate category proportions or classify Twitter users. Although some aspects of the analysis were carried out on our own, all model estimates come from proprietary classifiers provided on CH's platform. Most of the models are supervised, meaning they required human input in categorizing texts. Categorizing human expression using automated methods can be a tricky business, especially in languages like Arabic where multiple dialects exist in our texts. We take as many steps as we can to ensure that our results are accurate. For each model requiring human coding, we followed a rigorous training procedure with two Arabic-speaking coders who classified hundreds of Twitter posts for this project. Information on each model are provided in the applicable sections of the paper, and additional information and example training texts can be found in the online appendix. Additionally, for tasks where classification is so tricky as to make automated methods untenable—such as in our detailed content analysis in section 6, where we attempt to

7 parse conspiratorial and non-conspiratorial linkages of the US and ISIS—we eschew automated methods for a more qualitative approach. Nothing is one-hundred percent accurate, but we believe the steps we’ve taken to be the best possible approach given our data and analysis platform.

Although social media datasets allow for unprecedented access to data on thousands or even millions of individuals, there are limitations to social media data. First, the conclusions we draw might be limited by who uses Twitter in the Middle East. This concern is tempered by the fact that for our period of interest—the years 2014–2015—Twitter use was relatively widespread throughout the Middle East. Outside of the gulf, results from the 2016 Arab Barometer (the closest to our date range of interest) indicate that sizable portions of the population in Lebanon (27%), Palestine

(25%), Egypt and Algeria (both 19%), Morocco and Tunisia (both 17%), and Jordan (15%) reported having a Twitter account.6 In the gulf states, and especially in Saudi Arabia, the UAE, and

Kuwait, Twitter usage has been even higher from an even earlier date (Dubai School of Govern- ment 2012). This indicates that our sample of Twitter users covers a broad swath of the Arab

Middle East, while over-representing the gulf states and, as with much of social media, skewing younger than the general population. One strength of our design is that it primarily relies on the existence of correlation in sentiment toward ISIS and the US, rather than trying to estimate the proportion of the population that holds these sentiments. For this reason, even if Arabic-speaking

Twitter users are not representative of the Arab Middle East as a whole, the correlational patterns we find in our data can still hold.

Some may be concerned about Twitter bots, i.e. automated accounts, because ISIS was no- torious for its use of bots to spread its extremist propaganda. However, the bad publicity Twitter

6 As a point of comparison, in the US, 23% of all Internet users reported using Twitter in 2015 (Duggan 2015).

8 received over ISIS's use of its platform led to large-scale action targeting ISIS bots in 2014 and

2015 (Berger and Morgan 2015; Berger and Perez 2016). Additionally, the result we find—that negativity toward ISIS is correlated with negativity toward the US—is the opposite of the result one would expect if our findings were driven by ISIS bots. Lastly, we break down our results by frequency of Twitter posts in our main analysis (Section 5), and the correlation we find exists regardless of the frequency with which a user posts to Twitter. Bots are unusually prolific Twitter users; for instance, Berger and Morgan (2015, 28) find that the typical ISIS account Tweets 7.3 times per day (over 5,000 times during our period of interest). If ISIS bots were the reason for our findings, one would expect our correlational patterns to disappear for low-frequency users, which they do not.7

4. Aggregate Analyses

There are three main models we present in this section, all of which are based on the supervised algorithm described by Hopkins and King (2010). Each examines data at the aggregate level, where our quantity of interest is the percentage of posts (Tweets) that fall into certain categories.

Of these analyses, one examines posts about the US (US Analysis), a second examines posts about

ISIS (ISIS Analysis), and a third examines posts containing references to both entities (Combined

Analysis) from January 1, 2014 to December 31, 2015. These analyses use hundreds of user-trained posts that coders categorize into negative, neutral, and positive categories. The model then uses the training texts to categorize the universe of posts for each analysis. For the US Analysis, we differentiate between political and social content in the neutral and negative categories. For the

Combined Analysis, we omit the positive category (for lack of posts) and add negative categories

7 See section 6.2 of the online appendix for a more detailed look at this.

9 based on the entities with which the US is associated, differentiating posts that associate the US with ISIS from those that associate it with another disliked entity (e.g. the Syrian Regime, Iran, or

Shias) and from those that do not associate the US with any entity.

The results from these aggregate analyses, which are shown in Table 1, demonstrate a high degree of negativity toward both the US and ISIS. In the US Analysis (column 1), over two-thirds of the traffic is negative, mostly political in nature; the rest is neutral, with negligible positive traffic. In the ISIS Analysis (column 2), over half of the traffic is neutral, and of the traffic that expresses an opinion (i.e. excluding the neutral traffic), eighty percent is negative. Looking at posts that mention both entities in the Combined Analysis (column 3), we find that about 40% of the traffic expresses a negative opinion about at least one of the entities. Of this negative traffic, over half associates the US with ISIS or another disliked entity, while just under half does not associate the US with anyone.

Table 1 – Results of the Aggregate Analyses US Analysis ISIS Analysis Combined Analysis (N = 88,092,712) (N = 103,228,106) (N = 5,832,491) 1a. Neg. Pol. 60% 1. Neg. 34% 1a. Neg., US & ISIS 12% Negative 1b. Neg. Soc. 11% 1b. Neg., US & Syria/Iran/Shia 11% 1c. Neg., Other 18% 2a. Neu. Pol. 28% 2. Neu. 58% 2. Neutral 58% Neutral 2b. Neu. Soc. 1% Positive 3. Pos. <1% 3. Pos. 8% 3. N/A Notes: Abbreviations used for categories are Neg. = Negative, Pol. = Political, and Soc. = Social. Columns do not sum to 100% due to rounding.

5. Group-Based Analysis

To examine the correlation between users’ views towards the US and ISIS, we use the textual analysis models from the Aggregate Analyses on subsets of users defined by post frequency

10 and levels of negativity toward ISIS, an analysis that we refer to as the Group-Based Analysis. For this analysis, we have access to a very large sample— a randomly selected set of 1,078,832 posts

(262,906 unique users) from the ISIS Analysis—rather than the population. Using this sample, we define sixteen groups of users, classified by frequency and negativity, as seen in Table 2. The frequency data is highly skewed, as is common across Twitter as well as other social media plat- forms. Most users fall into the very low category (1-3 posts), with only a small proportion having more than 10 posts. Individuals’ negativity toward ISIS is estimated using a proprietary classifier, which necessarily differs from the model used in the ISIS Analysis because the unit of analysis is now users, not category proportions.8 Our choice to divide users into these sixteen groups was data-based; other grouping choices would not provide us with at least 1,000 users in each of the sixteen analysis groups. With these groups set, we then examine US-ISIS sentiment correlation by estimating the category proportions from the US Analysis model for each of the sixteen groups.

This approach is superior to alternatives: a purely aggregate-level approach could suffer from an ecological fallacy problem, while an individual-level approach would only be possible among high-frequency users.

Table 2 – User groups for the Group-Based Analysis % Negative Traffic: Very Low Low Medium High Sample Volume: (~0%) (0–40%) (40–60%) (60–100%) Very Low (1–3) 134,181 (51%) 4,557 (2%) 13,092 (5%) 78,933 (30%) Low (4–5) 4,066 (2%) 4,006 (2%) 2,485 (<1%) 1,946 (<1%) Medium (6–10) 2,237 (<1%) 3,789 (1%) 2,104 (<1%) 1,599 (<1%) High (>10) 1,466 (<1%) 4,973 (2%) 2,685 (1%) 1,279 (<1%) Notes: Percentages equal the proportion of the 262,906 unique users in each cell. Because of rounding, percentages do not add to 100%.

8 More information on both models can be found in section 2 of the online appendix.

11 The results of the Group-Based Analysis are found in Figure 1. The figure shows percent negative political US traffic on the y-axis and, on the x-axis, negativity towards ISIS

(measured as a ratio). The colors differentiate the groups by number of posts in the sample. Across all levels, there is a strong positive relationship between negativity toward the US and ISIS. For the lowest-volume group (1–3 sample posts), a move from lowest to highest ISIS negativity is associated with a 39.4 percentage point increase in political negativity in the US Analysis; for those with 4–5 posts, the increase is 82.6 percentage points; for those with 6–10 posts, it is 83.8 percentage points; and, for those with more than 10 posts, it is 82.8 percentage points. In general, the correlation is stronger for those with more than three posts—a finding which suggests that our correlation is not driven by neutral news reporting in the “low negativity” group, since news organizations generate a large amount of traffic. In the online appendix, we also check to make sure this pattern is not driven by the choice of our 16 analysis groups by examining the US-ISIS sentiment relationship at the individual level for 80 randomly-sampled high-volume users (>10 ISIS sample posts). What we find—an increase of 69.4 points in average negative political US traffic as you move from lowest to highest ISIS negativity level—closely mirrors the results in the Group-

Based Analysis, indicating that this pattern holds regardless of our choice of analysis method.

Figure 1 – Percent negative political traffic in the Group-Based Analysis.

12 We therefore argue that negativity toward the US is positively correlated with negativity toward ISIS. This conclusion is robust to several alternative hypotheses, the most plausible being that the correlation comes from users who are negative toward everything—imagine the rants of an “angry uncle” on Twitter. If this angry uncle hypothesis were true, we would expect to see a correlation between negativity toward ISIS and the negativity of users’ non-US and non-ISIS traffic. To test this, we apply a sentiment analysis model on all Arabic posts not including our keywords for each of our sixteen analysis groups. This automated model uses over 500,000 human- trained texts to estimate general sentiment, as opposed to a user-coded set of training texts as used for our aggregate analyses.9 We apply the same model to US traffic for a fair comparison, and the results from this test do not support the angry uncle hypothesis. Going from the lowest to highest

ISIS negativity level is associated with an average increase of 10 percentage points in general negative traffic, compared to 17 percentage points for negative US traffic. This difference only increases in size if comparing to the user-trained US Analysis instead.

There is also strong evidence against two additional hypotheses. First, we consider that individuals’ interest in international politics may explain the US-ISIS sentiment correlation—i.e. those interested in expressing an opinion express negativity, so interest is really what matters. If true, this would imply that negativity toward ISIS is correlated with how frequently users post about the US; the data indicate that the opposite is true, with the number of posts mentioning the

US going down as users, and especially high-frequency users, become more negative toward ISIS.

Second, we consider the possibility that Shia Twitter users, who could be more negative toward both the US and ISIS than Sunni users, are driving the results. We test this at the country level and

9 More information on this model can be found in section 2 of the online appendix

13 find that, on average, countries with small Shia populations have the same correlation between US and ISIS negativity as those with large ones.10

6. Behind the Correlation

What is behind this US-ISIS sentiment correlation? In this section, we evaluate two possible interpretations. (1) Independent negativity: In spite of the US's engagement against ISIS, users do not make connections between the two entities in their negativity. Negativity toward the two is independent. (2) Linked negativity: Some connect their negativity toward the two entities because they hold the US responsible for ISIS. Two possible version of linked negativity are also possible.

(A) Non-conspiratorial: Users believe that US intervention in the region has benefited ISIS, but do not believe that the US is directly intervening in favor of ISIS. (B) Conspiratorial: Users believe that the US and ISIS are in cahoots, with the US taking steps to purposefully help ISIS. We use data from one of our aggregate analyses and from an in-depth examination of a set of randomly- sampled users with high negativity toward ISIS to show that there is a surprisingly large number of users who believe in a conspiratorial interpretation.

First, we look at the Combined Analysis to address how often users take independent versus linked interpretations when both entities are mentioned in the same post. Table 3 summarizes the breakdown of negative posts mentioning both entities (these results corresponding to the first row and third column of Table 1). In almost half of the negative traffic, both the US and ISIS are mentioned but no connection is made between the US and other entities. However, almost a third of the negative traffic makes an association between the US and ISIS, while just over one quarter

10Section 6 of the online appendix provides details on all alternative hypotheses.

14 of it associates the US with other disliked entities. Many of these posts are conspiratorial in nature, while others merely indicate that US policy in the region has led to ISIS’s formation.

The Combined Analysis indicates that, for those on Twitter who express an opinion about both entities, independence is most common while at the same time a significant number of users connect the US and ISIS. However, there are limitations in relying solely on the Combined Anal- ysis to examine US-ISIS sentiment correlation. The Combined Analysis only examines posts containing references to both the US and ISIS, a relatively small subset of posts and one which may be inflating our sense of how often users associate the two entities. The Combined Analysis also suffers from the same level-of-analysis issues as the Aggregate Analysis, i.e. results are at the level of the text, not the individual. Additionally, parsing out the two versions of the linked negativity interpretation—non-conspiratorial vs. conspiratorial—is a difficult task, one for which an automated textual analysis model is not well-suited. For this reason, our two coders also conduct a detailed content analysis of posts from 100 users, 25 randomly sampled from each of our high

ISIS-negativity groups (i.e. the final column of Table 2). For each of these users, we examine their

Twitter posts mentioning either the US or ISIS to determine how they link the two entities. For many of the users, this involved reading dozens or even hundreds of posts for those with a larger number of posts in the sample. We note, for a given user, whether (1) the user linked the US and

ISIS conspiratorially in any Tweet (“Linked – Conspiratorial”); (2) the user never made a conspiratorial linkage, but did link the US and ISIS non-conspiratorially (“Linked – Non-Conspiratorial”); or (3) the user refrained from linking the two entities in any of the Tweets we read.11 This analysis

11 Determining what constitutes a “conspiratorial” linkage is a difficult endeavor; for our purposes, we define any post that includes clearly false claims suggesting that the US created or directly supports ISIS as conspiratorial. See section 5 of the online appendix for more detail.

15 can help us answer the following question: Among our users who are most negative toward ISIS, how many of them resort to conspiratorially linking the US and ISIS in at least some of their

Tweets? Table 4 summarizes the results of this analysis, with the results broken down by the frequency with which the users Tweeted in our sample from the ISIS Analysis.12

From this detailed content analysis, we draw a few conclusions. First, among this group of users who are particularly negative toward ISIS, linking the US and ISIS is common. Out of 100 high-negativity users, almost half (48 users) have at least one post (and usually more) linking the two entities. Additionally, these data strongly indicate that those who link the US and ISIS tend to do so in a conspiratorial manner—only 5 of the 48 users who linked the US and ISIS avoided resorting to conspiracy theories. Lastly, these data may indicate a relationship between the frequency with which one posts and the tendency to link the US and ISIS. Out of users whose posting frequency was “Very Low” or “Low” in the sample, there are 20 combined that connect the US and ISIS; for those with “Medium” or “High” frequency there are 28 combined that do so. This difference is relatively small and therefore readers should exercise caution in drawing conclusions

Table 3 – Focus of Negative Posts from Combined Analysis US associated with ISIS 29% US associated with Syria, Iran, and/or Shia 27% No association between US and other entities 44%

Table 4 – Summary of Users in Detailed Content Analysis Frequency Group: Category: 1 to 3 4 to 5 6 to 10 Above 10 Total Linked – Conspiratorial 9 9 13 12 43 Linked – Non-Conspiratorial 1 1 1 2 5 No Linkage 15 15 11 11 52

12 For a detailed breakdown along with example posts, see the online appendix materials.

16 from it, but it supports recent work (Guess et al. 2019) indicating a correlation of high social media engagement with endorsing conspiracy theories and other toxic messages.

Delving into the posts themselves reveals the context in which Arabic-speaking Twitter users make conspiratorial versus non-conspiratorial links between the US and ISIS. Uniting both sets of users is a focus on US intervention, as can be seen in the sample of non-conspiratorial posts displayed in Table 5.13 Three points about these posts deserve highlighting. First, whether it is US intervention in Iraq (posts 1, 2, and 4) or purported US support for armed groups in Syria (post 3), the negative results of US intervention are seen as creating an opportunity for ISIS to grow. Sec- ond, some of these posts highlight the existence of US sources (a former US DIA director in post

3; Michael Moore in post 4) who agree with them, seemingly as a way of legitimizing their point.

Lastly, The US is one of several entities (Russia in post 1; Houthis in post 2) that these users view

Table 5 – Sample of Posts Linking the US and ISIS Non-Conspiratorially ﺗﺪ ّﺧﻞ اﻻﺗﺤﺎد اﻟﺴﻮﻓﯿﺘﻲ ﻓﻲ اﻓﻐﺎﻧﺴﺘﺎن The Soviet Union’s intervention in Afghanistan brought .1 ﺞﺘﻧا ﺎﻨﻟ .ةﺪﻋﺎﻘﻟا ﻞﺧﺪﺗو ﺎﻜﯾﺮﻣا ﻲﻓ us al-Qaeda. America’s intervention in Iraq brought us قاﺮﻌﻟا ﺞﺘﻧا ﺎﻨﻟ .ﺶﻋاد اذﺎﻣ ﺞﺘﻨﯿﺳ ﺎﻨﻟ ISIS. What is the Russians’ intervention in Syria going ﻞﺧﺪﺗ ا سوﺮﻟ ﻲﻓ ﺎﯾرﻮﺳ ؟!. ?to bring us

ﺮﻣﺄﺑ نﺎﻜﯾﺮﻣﻻا ﺖﻤﻠﺳ ﺔﻣﻮﻜﺣ ﻲﻜﻟﺎﻤﻟا American military orders resulted in al-Malaki handing .2 لﺎﻤﺷ ا قاﺮﻌﻟ ﻟ ﺪ ﺶﻋا ﺪﻌﺑ ا بﺎﺤﺴﻧ over North Iraq to Daesh. Similar orders in Yemen left ﺶﯿﺠﻟا ﻚﻟﺬﻛو ﺖﻠﻌﻓ ﻲﻓ ﻦﻤﯿﻟا ﻦﯿﺣ the Houthis in control تﺮﻣأ ا ﺶﯿﺠﻟ ﻢﯿﻠﺴﺘﺑ ءﺎﻌﻨﺻ ﻟ ﻲﺛﻮﺤﻠ

ﺪﻣ ﺮﯾ ﺎﺳ ﻖﺑ ﺎﻛﻮﻟ ﻟ ﺔ ﺒﺨﺘﺳإ تارﺎ ا ﻟ ﻓﺪ عﺎ A former director of the US Defense Intelligence .3 :ﺔﯿﻜﯾﺮﻣﻻا ةرادإ ﺑوأ ﺎﻣﺎ ﺖﻤﻋد ا ﻟ ﻘ ةﺪﻋﺎ Agency: The Obama administration supported al-Qaeda واﻹﺧﻮان ﻓﻲ ﺳﻮرﯾﺎ و ﻣ ّﻜﻨﺖ داﻋﺶ ﻣﻦ and the Muslim Brotherhood in Syria and made ISIS’s زوﺮﺒﻟا rise possible

جﺮﺨﻤﻟا ﻲﻜﯾﺮﻣﻷا ﻞﻜﯾﺎﻣ رﻮﻣ ﻊﻀﯾ The American director Michael Moore displays a picture .4 ﺻﻮرة ﻻﺟﺌﯿﻦ ﺳﻮرﯾﯿﻦ ُﻛﺘﺐ ﻓﯿﮭﺎ: ﻟﻢ of Syrian refugees, on which was written “they didn’t اﻮﻌﻨﺼﯾ د .ﺶﻋا ﻊﻣ ةرﻮﺻ ﻟ شﻮﺒ ﻧو ﺎ ﺋ ﮫﺒ create ISIS,” next to a picture of Bush and his Vice ُﻛﺘﺐ ﻋﻠﯿﮭﺎ: ﺑﻞ ﻧﺤﻦ! .”!President, on which was written “they did

13 For the sake of readability, hashtags that are not essential to the text as well as links have been removed from the posts in Tables 5 and 6.

17 as impinging on the sovereignty and stability of Middle Eastern states. These users do not necessarily link the US and these additional entities, but they do consider their actions comparable. For instance, in the first post, America’s intervention in Iraq serves as an example of what might occur with Russian intervention in Syria.

Examining the conspiratorial posts in Table 6 indicates similarities and differences with the non-conspiratorial posts. Similar to non-conspiratorial posts linking the US and ISIS, these conspiratorial posts also focus on US intervention in the region and also mention additional entities threatening the sovereignty or stability of Middle Eastern States. However, the conspiratorial posts differ in three important ways from their non-conspiratorial counterparts. First, the additional entities mentioned not only threaten Middle Eastern states independently—they are in cahoots with the US. Whether it is Iran and the West (post 1), Israel and the US (post 2), or the US

Table 6 – Sample of Posts Linking the US and ISIS Conspiratorially ﺶﻋاد مﻮﻘﺗ ﻞﻤﻌﺑ ﻲﺗاﺮﺑﺎﺨﻣ ﻲﻧاﺮﯾا ISIS is carrying out an Iranian-Western intelligence .1 - ﻲﺑﺮﻏ بﺮﻀﻟ ا ﺔﻨﺴﻟ ﮫﯾﻮﺸﺗو operation to strike against the Sunnis and tarnish their ﻤﺳ ﻌ ﺘ ﮭ ﻢ ا ﻣ ﺎ م ا ﻟ ﻌ ﺎ ﻟ ﻢ reputation in front of the world

ﺶﻋاد ﺔﻋﺎﻨﺻ ﻮﯿﮭﺻ ﺔﯿﻜﯾﺮﻣا ISIS is a Zionist and American creation and it is apparent .2 او ﺮھﺎﻈﻟ نا ازاﻮﺟ ﻢﮭﺗ ﺔﻋﻮﺒﻄﻣ ﻲﻓ .that (even) their passports are printed in Israel or the US اﺮﺳا ﺋ ﻞﯿ وا ا ﺎﻜﯾﺮﻣ

ﻨﺳ ﺮﺸﻨ ﺪﻌﺑ ﻗ ﻠ ﻞﯿ ﻓ ﻮﯾﺪﯿ ﺮﯿﻄﺧ ﻓو ﮫﯿ We will soon be publishing a dangerous video containing .3 ﺔﺤﯿﻀﻓ ىﺮﺒﻛ ﻦﻋ ﯾﺮط ﺔﻘ ﺪﺨﺘﺳا ا م details of] a major scandal about the way that America used] ﺎﻜﯾﺮﻣأ ﺶﻋاﺪﻟ ﻟ ﺎﮭﻠﻌﺠﺘ ﺂﻏﻮﺴﻣ ﺎﮭﻟ – ISIS as a justification for direct intervention in the region ﻞﺧﺪﺘﻠﻟ ﺮﺷﺎﺒﻤﻟا ﻲﻓ ﺔﻘﻄﻨﻤﻟا هوﺪھﺎﺷ watch it and focus well وﺰﻛرو ا ﺟ ﯿ ﺪ آ

ﺎﻜﯾﺮﻣأ ﻲﻋﺪﺗ بﺮﺣ ﺶﻋاد# !! America claims to be fighting ISIS! I believe the more .4 ﺘﻋأ ﺪﻘ نأ ا ﺒﻌﻟ ةرﺎ ا ﺔﺤﯿﺤﺼﻟ اﺬﻜھ : correct expression would be: America is supporting ISIS ﺎﻜﯾﺮﻣأ يﺬﻐﺗ ﺶﻋاد#

ﺗﻻ اﻮﺴﻨ ﺎﻋ م 2014 ﺎﻜﯾﺮﻣا ﺎﺑرواو Don’t forget the year 2014, [in which] America and Europe .5 ﻨﺻ ﻌ ﻮ ا د ا ﺶﻋ ﺪﯾﺮﺸﺘﻟ بﺮﻌﻟا created ISIS in order to displace, kill, and rob money and oil ﻗو ﺘ ﻢﮭﻠ ﺐﮭﻧو اﻮﻣا ﻢﮭﻟ ﻧو ﻢﮭﻄﻔ نﺎﻛ from Arabs – it was the year in which we were truly مﺎﻋ ا ءﺎﻀﻘﻟ ا ﻲﻘﯿﻘﺤﻟ ﻠﻋ ﺎﻨﯿ او ﻟ ﺔﯾﺎﮭﻨ eliminated, and which ended with the vile veto [reference to ﻮﺘﯿﻔﻟا .ﺮﯿﻘﺤﻟا [veto of UN resolution condemning Asad regime

18 and Europe (post 5), these users posit a broad and variegated coalition of outside forces threatening the Middle East, Arabs, and Sunni Muslims. Second, many of these users attribute primary responsibility for ISIS’s actions to the outside forces mentioned rather than to ISIS itself. In posts 1 and

5, for instance, ISIS is merely a pawn in an operation by these outside forces (including the US) to defame Sunnis and terrorize Arabs. Lastly, these users posit sinister, secretive motives behind

US actions that are not as present in the non-conspiratorial posts. For users positing a conspiratorial linkage between the US and ISIS, the US’s actions regarding ISIS go against its public claims

(post 4), are part of secretive intelligence plots by multiple foreign entities (post 1), and are dangerous to even post about by those who choose to “tell the truth” (post 3).

This detailed content analysis has important limitations—the posts presented are just a snap- shot from a 100-user sample. However, the fact that almost half of the users in this sample link the

US and ISIS, most of them conspiratorially, means that even a larger sample would likely contain similar patterns. A conspiratorial view of the US’s role in the region, one that sees the US as in cahoots with other intervening foreign entities and engaged in sinister plots against Arabs and

Sunnis in the region, is common among those who are simultaneously negative toward ISIS.

7. Temporal Variation

As a final note, we examine how our results vary across time. The variation of our results across time in both the aggregate analyses and the Group-Based Analysis help illustrate the anti- interventionist nature of negative content. Figure 2 shows the percent negative traffic for the ISIS

Analysis, the US Analysis, and high ISIS-negativity users from the Group-Based Analysis. Two insights are notable. First, in all three trend lines, negative traffic peaks as the US begins its military intervention against ISIS in mid-2014. This is the time period where the US began targeted strikes

19 in Syria and manned missions and ground assistance in Iraq against Islamic State targets, in response to requests from the Iraqi government. Spikes in negative traffic for the US Analysis con- tinue in August and September, when the US and allies began large-scale airstrikes against Islamic

State targets in both countries. Second, in the ISIS Analysis, high negative traffic also follows ISIS attacks and killings in the West (e.g. the Nov. 2015 attacks in France) and the Middle East (e.g. the killing of a Jordanian pilot in Jan. 2015).14 These results mirror findings that US intervention drives anti-Americanism in the Middle East (Jamal et al. 2015) and that ISIS attacks decrease its support (Barceló and Labzina 2018).

Figure 2 – Percent negative traffic in the ISIS, US, and Group-Based Analyses.

8. Conclusion

For Arabic Twitter users posting about either ISIS or the United States during 2014–2015,

“the enemy of my enemy is not my friend.” On the contrary, negative views of ISIS and the United

States are positively correlated and this correlation is not spurious. Many Arabic Twitter users independently dislike both the United States and ISIS, but a sizeable proportion also believe in conspiratorial covert linkages between these two disliked entities.

14These temporal dynamics are further explored in section 4.1 of the online appendix.

20 These findings were unexpected and warrant further research on how publics in the Middle

East and elsewhere interpret information they receive. Accounts of politics that seem implausible to well-informed outside observers often have great appeal to people with strong prior views.

Widespread acceptance of fake and inaccurate news can have a major impact on politics in the

Middle East in particular, as well as elsewhere. Understanding how such views emerge and spread, and how they shape citizens’ interpretations of the actions of foreign states and non-state actors, is an important subject for continued research.

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Online Appendix

For the paper “The Enemy of my Enemy Is Not My Friend: Arabic Twitter Sentiment Toward ISIS and the United States”

Table of Contents 1 Introduction ...... 3 2 Textual Analysis Models ...... 4 3 Training Procedure for Main Analyses ...... 5 3.1 General Procedure ...... 5 3.2 US Analysis (753 posts) ...... 5 3.2.1 Keywords ...... 5 3.2.2 Example Training Texts ...... 6 3.3 ISIS Analysis (621 posts) ...... 8 3.3.1 Keywords ...... 8 3.3.2 Example Training Texts ...... 8 3.4 Combined Analysis (152 posts) ...... 10 3.4.1 Keywords ...... 10 3.4.2 Example Training Texts ...... 11 4 Additional Results from Main Analyses ...... 13 4.1 Temporal Variation in US and ISIS Analyses ...... 13 4.2 Aggregate Analysis of US-ISIS Sentiment Correlation ...... 14 4.3 Additional Results from the Group-Based Analysis ...... 16 5 Detailed Content Analysis ...... 19 6 Alternative Hypotheses ...... 20 6.1 The Ecological Fallacy Hypothesis ...... 20 6.2 The News Organization Hypothesis ...... 21 6.3 The “Angry Uncle” Hypothesis ...... 22

1 6.3.1 Keywords for “Angry Uncle” Analysis ...... 23 6.4 The Salience of International Politics Hypothesis ...... 26 6.5 The Sunni-Shia Hypothesis ...... 27 7 ISIS Attacks ...... 28

2 1 Introduction

This appendix provides additional analyses and information for the paper “The enemy of my enemy is not my friend: Arabic Twitter sentiment towards ISIS and the United States.” In addition to what is available in this appendix, please examine the replication directory. This directory con- tains our replication script, which reproduces the numbers and figures within the paper itself as well as those in this appendix. Note that, because of Twitters terms of service, we cannot include datasets of Twitter posts in our replication files. Instead, we provide examples when necessary and include all other data possible.

3 2 Textual Analysis Models

We use computational textual analysis to estimate several different quantities of interest in the analyses for this paper. We employ three different textual analysis methods in our analyses, each of them suited to a different purpose in our project. Many elements of the algorithms used in these models are proprietary—we gain access to them as subscribers to Crimson Hexagon’s social media analytics platform, ForSight—and we are therefore limited in the amount of detail we can provide. However, we describe them in as much detail as possible below.

The first model (the ReadMe-Based Model) we use is a proprietary supervised model based on the ReadMe algorithm of Hopkins and King (2010). For this model, the user hand codes a set of documents into pre-determined categories, and the algorithm uses this set to estimate category proportions for the set of texts as a whole. Notably, this model does not classify individual documents. Instead, the algorithm produces daily population-based estimates. In the paper, the results of this model are often presented as daily counts; those counts are merely the product of the estimated category proportion and the total number of posts for a given day/period. In the paper, we use this model for the US, ISIS, and Combined Analyses.

The second model (the Researcher-Trained Classifier) we use is a proprietary supervised classifier, also available through Crimson Hexagon’s ForSight platform. To understand our use of this model, it helps to understand the access the platform provides for downloading and analyzing Twitter posts off of the platform. Access to the raw data is limited to 10,000 posts per day of any given analysis. These 10,000 posts per day are chosen at random from the posts that match one’s criteria for that analysis. The downloaded posts include a number of pieces of metadata estimated by the ForSight platform, including the geographical location of the user, a measure of the user’s social media influence, and (most importantly for our purposes) an estimate of the category of the post in the analysis. It is this category metadata that is estimated using the second model. This model uses the same training set and user-defined categories as the first model, and it is only the output of the models that differs (category proportions for the first vs. document-level classification for the second). In the paper, we use the results of this model to estimate user-level negativity toward ISIS for the users in Twitter posts we downloaded from our ISIS Analysis. As noted in the paper, we divide users into 16 groups based on the number of their posts in our downloaded sample as well as the estimated negativity of their posts. Then, for each group of users, we run the US Analysis model on their posts that include our US-related keywords. This allows us to correlate, at the group level, sentiment toward ISIS and the US.

The third and final model (the Sentiment Classifier) we use is another proprietary supervised sentiment classifier. This model uses over 500,000 Arabic training texts that were human-coded by Crimson Hexagon as having positive, neutral, or negative sentiment. These texts are not limited to Twitter and include posts from Facebook, online blogs, reviews, and news items, representing a broad swathe of online media. Like the second model, the output of this third model is a document- level classification of texts. In the paper, we use this model to address the alternative hypothesis that the correlation between negativity toward ISIS and the US that we observe in the group-based analysis is merely a function of negativity toward ISIS being associated with general negativity.

4 3 Training Procedure for Main Analyses 3.1 General Procedure

We conduct three main analyses using the ReadMe-based model: One examining posts about the US, a second examining posts about ISIS, and a third examining posts that mention both entities. Throughout our paper, these are referred to, respectively, as the US, ISIS, and Combined analyses. For each of these analyses, we adhere to the following procedure.

First, we specify a set of parameters that determines the texts included in our analysis. This in- volves specifying: • A date range of interest—we restrict this to January 1, 2014 to December 31, 2015. • Textual sources—we restrict this to Twitter only. • Language—we do not restrict language because our keywords, being mostly unique to Ar- abic, are sufficient to restrict our analysis to Arabic results (any Farsi results are trained into an “irrelevant” category). • Geographical restrictions—we do not restrict geographical location, meaning our results apply to the Arabic Twitter universe as a whole. • Keywords—the keywords varied by analysis, and we include the keywords for each analysis in its respective section below. Keywords for CH’s online platform use common Bool- ean expressions (AND/OR) to specify relationships between expressions, quotation marks to note expressions to be matched, mathematical operators (e.g. -) to exclude expressions, and parentheses to combine expressions. CH does not allow wildcards for Arabic language searches, meaning that we have to include a large number of iterations of each keyword to ensure that we are capturing all relevant traffic.

Second, we train randomly-selected documents into a set of user-defined categories. Two of the researchers each train the same posts for each of the three main analyses. This training is done blind to the categorization of the other researcher. After training, we reconcile any disagreements between the researchers’ training, coming to a unanimous agreement about disputed texts until we reach a final categorization for every text.

In the following sections, we list the keywords, number of total training posts, and example posts for each category 3.2 US Analysis

753 posts total. 3.2.1 Keywords ﺎﻜﯾﺮﻣﺎﺑ OR ﺎﻜﯾﺮﻣﺄﺑ OR ﻣﻻ ﯿ ﻛﺮ ﺎ OR ﻣﻷ ﯿ ﻛﺮ ﺎ OR ﺮﻣﻻ ﯾ ﻜ ﺎ OR ﺮﻣﻷ ﯾ ﻜ ﺎ OR ﺎﻛﺮﯿﻣا OR ﺎﻛﺮﯿﻣأ OR ﺎﻜﯾﺮﻣا OR ﺮﻣأ ﺎﻜﯾ ) OR ﺎﻛﺮﯿﻣأو OR ﺎﻜﯾﺮﻣاو OR ﺎﻜﯾﺮﻣأو OR ﺎﻛﺮﯿﻣﺎﻓ OR ﺎﻛﺮﯿﻣﺄﻓ OR ﺎﻜﯾﺮﻣﺎﻓ OR ﺎﻜﯾﺮﻣﺄﻓ OR ﺎﻛﺮﯿﻣﺎﺑ OR ﺎﻛﺮﯿﻣﺄﺑ OR ﻲﻛﺮﯿﻣﻻا OR ﻲﻛﺮﯿﻣﻷا OR ﻲﻛﺮﯿﻣا OR ﻲﻛﺮﯿﻣأ OR ﻲﻜﯾﺮﻣﻻا OR ﻲﻜﯾﺮﻣﻷا OR ﻲﻜﯾﺮﻣا OR ﻲﻜﯾﺮﻣأ OR ﺎﻛﺮﯿﻣاو OR ﻲﻛﺮﯿﻣﻼﻟ OR ﻲﻛﺮﯿﻣﻸﻟ OR ﻣﻻ ﯿ ﻲﻛﺮ OR ﻣﻷ ﯿ ﻲﻛﺮ OR ﯾﺮﻣﻼﻟ ﻲﻜ OR ﯾﺮﻣﻸﻟ ﻲﻜ OR ﺮﻣﻻ ﯾ ﻲﻜ OR ﺮﻣﻷ ﯾ ﻲﻜ OR OR ﻲﻛﺮﯿﻣﻻﺎﺑ OR ﻲﻛﺮﯿﻣﻷﺎﺑ OR ﻲﻛﺮﯿﻣﺎﺑ OR ﻲﻛﺮﯿﻣﺄﺑ OR ﻲﻜﯾﺮﻣﻻﺎﺑ OR ﻲﻜﯾﺮﻣﻷﺎﺑ OR ﻲﻜﯾﺮﻣﺎﺑ OR ﻲﻜﯾﺮﻣﺄﺑ

5 OR ﻲﻛﺮﯿﻣﻻﺎﻓ OR ﻲﻛﺮﯿﻣﻷﺎﻓ OR ﻲﻛﺮﯿﻣﺎﻓ OR ﻲﻛﺮﯿﻣﺄﻓ OR ﻲﻜﯾﺮﻣﻻﺎﻓ OR ﻲﻜﯾﺮﻣﻷﺎﻓ OR ﻲﻜﯾﺮﻣﺎﻓ OR ﻲﻜﯾﺮﻣﺄﻓ OR ﻲﻛﺮﯿﻣﻻاو OR ﻲﻛﺮﯿﻣﻷاو OR ﻲﻛﺮﯿﻣاو OR ﻲﻛﺮﯿﻣأو OR ﻲﻜﯾﺮﻣﻻاو OR ﻲﻜﯾﺮﻣﻷاو OR ﻲﻜﯾﺮﻣاو OR ﻲﻜﯾﺮﻣأو ﺮﻣﻷ ﯾ ﻜ ﯿ ﺔ OR ﯿﻛﺮﯿﻣﻻا ﺔ OR ﯿﻣﻷا ﻛﺮ ﯿ ﺔ OR ﺔﯿﻛﺮﯿﻣا OR ﺔﯿﻛﺮﯿﻣأ OR ﯿﻜﯾﺮﻣﻻا ﺔ OR ﯿﻜﯾﺮﻣﻷا ﺔ OR ﺔﯿﻜﯾﺮﻣا OR ﺔﯿﻜﯾﺮﻣأ OR ﺔﯿﻜﯾﺮﻣﺄﺑ OR ﻛﺮﯿﻣﻼﻟ ﯿ ﺔ OR ﻛﺮﯿﻣﻸﻟ ﯿ ﺔ OR ﻣﻻ ﯿ ﻛﺮ ﯿ ﺔ OR ﻣﻷ ﯿ ﻛﺮ ﯿ ﺔ OR ﯾﺮﻣﻼﻟ ﻜ ﯿ ﺔ OR ﯾﺮﻣﻸﻟ ﻜ ﯿ ﺔ OR ﺮﻣﻻ ﯾ ﻜ ﯿ ﺔ OR OR ﺔﯿﻜﯾﺮﻣﺄﻓ OR ﺔﯿﻛﺮﯿﻣﻻﺎﺑ OR ﺔﯿﻛﺮﯿﻣﻷﺎﺑ OR ﺔﯿﻛﺮﯿﻣﺎﺑ OR ﺔﯿﻛﺮﯿﻣﺄﺑ OR ﺔﯿﻜﯾﺮﻣﻻﺎﺑ OR ﺔﯿﻜﯾﺮﻣﻷﺎﺑ OR ﺔﯿﻜﯾﺮﻣﺎﺑ OR ﺔﯿﻜﯾﺮﻣأو OR ﺔﯿﻛﺮﯿﻣﻻﺎﻓ OR ﺔﯿﻛﺮﯿﻣﻷﺎﻓ OR ﺔﯿﻛﺮﯿﻣﺎﻓ OR ﺔﯿﻛﺮﯿﻣﺄﻓ OR ﺔﯿﻜﯾﺮﻣﻻﺎﻓ OR ﺔﯿﻜﯾﺮﻣﻷﺎﻓ OR ﺔﯿﻜﯾﺮﻣﺎﻓ OR ﻦﯿﯿﻜﯾﺮﻣأ OR ﺔﯿﻛﺮﯿﻣﻻاو OR ﺔﯿﻛﺮﯿﻣﻷاو OR ﺔﯿﻛﺮﯿﻣاو OR ﺔﯿﻛﺮﯿﻣأو OR ﺔﯿﻜﯾﺮﻣﻻاو OR ﺔﯿﻜﯾﺮﻣﻷاو OR ﺔﯿﻜﯾﺮﻣاو OR ﺮﻣﻷ ﯾ ﻜ ﯿ ﯿ ﻦ OR ﯿﻛﺮﯿﻣﻻا ﻦﯿ OR ﯿﻛﺮﯿﻣﻷا ﻦﯿ OR ﻦﯿﯿﻛﺮﯿﻣا OR ﻦﯿﯿﻛﺮﯿﻣأ OR ﯿﻜﯾﺮﻣﻻا ﻦﯿ OR ﯿﻜﯾﺮﻣﻷا ﻦﯿ OR ﻦﯿﯿﻜﯾﺮﻣا ﻦﯿﯿﻜﯾﺮﻣﺄﺑ OR ﻛﺮﯿﻣﻼﻟ ﯿ ﻦﯿ OR ﻛﺮﯿﻣﻸﻟ ﯿ ﻦﯿ OR ﻣﻻ ﯿ ﻛﺮ ﯿ ﯿ ﻦ OR ﻣﻷ ﯿ ﻛﺮ ﯿ ﯿ ﻦ OR ﯾﺮﻣﻼﻟ ﻜ ﯿ ﻦﯿ OR ﯾﺮﻣﻸﻟ ﻜ ﯿ ﻦﯿ OR ﺮﻣﻻ ﯾ ﻜ ﯿ ﯿ ﻦ ﻣ OR ﻦﯿﯿﻛﺮﯿﻣﻻﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﻷﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﺄﺑ OR ﻦﯿﯿﻜﯾﺮﻣﻻﺎﺑ OR ﻦﯿﯿﻜﯾﺮﻣﻷﺎﺑ OR ﻦﯿﯿﻜﯾﺮﻣﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﻻﺎﻓ OR ﻦﯿﯿﻛﺮﯿﻣﻷﺎﻓ OR ﻦﯿﯿﻛﺮﯿﻣﺎﻓ OR ﻦﯿﯿﻛﺮﯿﻣﺄﻓ OR ﻦﯿﯿﻜﯾﺮﻣﻻﺎﻓ OR ﻦﯿﯿﻜﯾﺮﻣﻷﺎﻓ OR ﻦﯿﯿﻜﯾﺮﻣﺎﻓ OR ﺮﻣﺄﻓ ﯿﻜﯾ ﻦﯿ ﻜ OR ﯿﻛﺮﯿﻣﻷاو ﻦﯿ OR ﯿﻛﺮﯿﻣاو ﻦﯿ OR ﯿﻛﺮﯿﻣأو ﻦﯿ OR ﯿﻜﯾﺮﻣﻻاو ﻦﯿ OR ﯿﻜﯾﺮﻣﻷاو ﻦﯿ OR ﯿﻜﯾﺮﻣاو ﻦﯿ OR ﯿﻜﯾﺮﻣأو ﻦﯿ OR نﻮﯿﻛﺮﯿﻣﻷا OR نﻮﯿﻛﺮﯿﻣا OR نﻮﯿﻛﺮﯿﻣأ OR نﻮﯿﻜﯾﺮﻣﻻا OR نﻮﯿﻜﯾﺮﻣﻷا OR نﻮﯿﻜﯾﺮﻣا OR نﻮﯿﻜﯾﺮﻣأ OR ﯿﻛﺮﯿﻣﻻاو ﻦﯿﯿﺮﻣا OR ﻣﻻ ﯿ ﻛﺮ ﯿ نﻮ OR ﻣﻷ ﯿ ﻛﺮ ﯿ نﻮ OR ﯾﺮﻣﻼﻟ ﻜ ﯿ نﻮ OR ﯾﺮﻣﻸﻟ ﻜ ﯿ نﻮ OR ﺮﻣﻻ ﯾ ﻜ ﯿ نﻮ OR ﺮﻣﻷ ﯾ ﻜ ﯿ نﻮ OR نﻮﯿﻛﺮﯿﻣﻻا OR OR نﻮﯿﻛﺮﯿﻣﺄﺑ OR نﻮﯿﻜﯾﺮﻣﻻﺎﺑ OR نﻮﯿﻜﯾﺮﻣﻷﺎﺑ OR نﻮﯿﻜﯾﺮﻣﺎﺑ OR نﻮﯿﻜﯾﺮﻣﺄﺑ OR ﻛﺮﯿﻣﻼﻟ ﯿ نﻮ OR ﻛﺮﯿﻣﻸﻟ ﯿ نﻮﯿﻛﯿﻸ OR نﻮﯿﻜﯾﺮﻣﻻﺎﻓ OR نﻮﯿﻜﯾﺮﻣﻷﺎﻓ OR نﻮﯿﻜﯾﺮﻣﺎﻓ OR نﻮﯿﻜﯾﺮﻣﺄﻓ OR نﻮﯿﻛﺮﯿﻣﻻﺎﺑ OR نﻮﯿﻛﺮﯿﻣﻷﺎﺑ OR نﻮﯿﻛﺮﯿﻣﺎﺑ OR نﻮﯿﻜﯾﺮﻣﻷاو OR نﻮﯿﻜﯾﺮﻣاو OR نﻮﯿﻜﯾﺮﻣأو OR نﻮﯿﻛﺮﯿﻣﻻﺎﻓ OR نﻮﯿﻛﺮﯿﻣﻷﺎﻓ OR نﻮﯿﻛﺮﯿﻣﺎﻓ OR نﻮﯿﻛﺮﯿﻣﺄﻓ نﺎﻜﯾﺮﻣﻷا OR نﺎﻜﯾﺮﻣا OR نﺎﻜﯾﺮﻣأ OR نﻮﯿﻛﺮﯿﻣﻻاو OR نﻮﯿﻛﺮﯿﻣﻷاو OR نﻮﯿﻛﺮﯿﻣاو OR نﻮﯿﻛﺮﯿﻣأو OR نﻮﯿﻜﯾﺮﻣﻻاو OR ﯾﺮﻣﻸﻟ ﻜ نﺎ OR ﺮﻣﻻ ﯾ ﻜ ﺎ ن OR ﺮﻣﻷ ﯾ ﻜ ﺎ ن OR نﺎﻛﺮﯿﻣﻻا OR نﺎﻛﺮﯿﻣﻷا OR نﺎﻛﺮﯿﻣا OR نﺎﻛﺮﯿﻣأ OR نﺎﻜﯾﺮﻣﻻا OR OR نﺎﻜﯾﺮﻣﻷﺎﺑ OR نﺎﻜﯾﺮﻣﺎﺑ OR نﺎﻜﯾﺮﻣﺄﺑ OR ﻛﺮﯿﻣﻼﻟ نﺎ OR ﻛﺮﯿﻣﻸﻟ نﺎ OR ﻣﻻ ﯿ ﻛﺮ ﺎ ن OR ﻣﻷ ﯿ ﻛﺮ ﺎ ن OR ﯾﺮﻣﻼﻟ ﻜ نﺎ ﺮﻼ OR نﺎﻜﯾﺮﻣﻷﺎﻓ OR نﺎﻜﯾﺮﻣﺎﻓ OR نﺎﻜﯾﺮﻣﺄﻓ OR نﺎﻛﺮﯿﻣﻻﺎﺑ OR نﺎﻛﺮﯿﻣﻷﺎﺑ OR نﺎﻛﺮﯿﻣﺎﺑ OR نﺎﻛﺮﯿﻣﺄﺑ OR ﯾﺮﻣﻻﺎﺑ نﺎﻜ نﺎﻜﯾﺮﻣﻷاو OR نﺎﻜﯾﺮﻣاو OR نﺎﻜﯾﺮﻣأو OR نﺎﻛﺮﯿﻣﻻﺎﻓ OR نﺎﻛﺮﯿﻣﻷﺎﻓ OR نﺎﻛﺮﯿﻣﺎﻓ OR نﺎﻛﺮﯿﻣﺄﻓ OR نﺎﻜﯾﺮﻣﻻﺎﻓ تﺎﯾﻻﻮﻠﻟ " OR " تﺎﯾﻻﻮﻟا ةﺪﺤﺘﻤﻟا " OR ﻻاو نﺎﻛﺮﯿﻣ OR نﺎﻛﺮﯿﻣﻷاو OR نﺎﻛﺮﯿﻣاو OR نﺎﻛﺮﯿﻣأو OR نﺎﻜﯾﺮﻣﻻاو OR OR ﻦﻄﻨﺷاﻮﻟ OR ﻦﻄﻨﺷاو OR " او تﺎﯾﻻﻮﻟ ا ةﺪﺤﺘﻤﻟ " OR " تﺎﯾﻻﻮﻟﺎﻓ ةﺪﺤﺘﻤﻟا " OR " تﺎﯾﻻﻮﻟﺎﺑ ةﺪﺤﺘﻤﻟا " OR " ةﺪﺤﺘﻤﻟا ﺎﻣﺎﺑوﺄﺑ OR ﺑﻻ ﺎ ﻣ ﺎ OR وﻻ ﺑ ﺎ ﻣ ﺎ OR وﻷ ﺑ ﺎ ﻣ ﺎ OR ﺎﻣﺎﺑا OR ﺑوا ﺎﻣﺎ OR ﺑوأ ﺎﻣﺎ OR وو ﻦﻄﻨﺷا OR ﻦﻄﻨﺷاﻮﻓ OR ﻦﻄﻨﺷاﻮﺑ بﺮﻐﻟﺎﺑ OR بﺮﻐﻠﻟ OR بﺮﻐﻟا OR ﺎﻣﺎﺑا OR ﺑوا ﺎﻣﺎ OR ﺑوأ ﺎﻣﺎ OR ﺎﻣﺎﺑا OR ﺑوا ﺎﻣﺎ OR ﺑوأ ﺎﻣﺎ OR ﺎﻣﺎﺑا OR ﺑوا ﺎﻣﺎ OR OR ﻲﺑﺮﻐﻓ OR ﻲﺑﺮﻐﻟﺎﺑ OR ﻲﺑﺮﻐﺑ OR ﻲﺑﺮﻐﻠﻟ OR ﻲﺑﺮﻐﻟ OR ﻲﺑﺮﻐﻟا OR ﺮﻏ ﺑ ﻲ OR او بﺮﻐﻟ OR بﺮﻐﻟﺎﻓ OR ﺑﺮﻐﻓ ﺔﯿ OR ﺔﯿﺑﺮﻐﻟﺎﺑ OR ﺔﯿﺑﺮﻐﺑ OR ﺮﻐﻠﻟ ﺔﯿﺑ OR ﺔﯿﺑﺮﻐﻟ OR ﺔﯿﺑﺮﻐﻟا OR ﺮﻏ ﺑ ﯿ ﺔ OR او ﻲﺑﺮﻐﻟ OR ﺮﻏو ﻲﺑ OR ﻲﺑﺮﻐﻟﺎﻓ ﺖﯿﺒﻠﻟ " OR " ﺖﯿﺒﻠﻟ ﺾﯿﺑﻻا " OR " ﺖﯿﺒﻟا ﺾﯿﺑﻷا " OR " ﺖﯿﺒﻟا ﺾﯿﺑﻻا " OR او ﺔﯿﺑﺮﻐﻟ OR ﺮﻏو ﺑ ﯿ ﺔ OR ﺔﯿﺑﺮﻐﻟﺎﻓ OR " او ﻟ ﺒ ﺖﯿ ﺾﯿﺑﻻا " OR " ﺖﯿﺒﻟﺎﻓ ﺾﯿﺑﻷا " OR " ﺖﯿﺒﻟﺎﻓ ﺾﯿﺑﻻا " OR " ﺖﯿﺒﻟﺎﺑ ﺾﯿﺑﻷا " OR " ﺖﯿﺒﻟﺎﺑ ﺾﯿﺑﻻا " OR " ﺑﻷا ﺾﯿ ﻷ - AND " رﻻود أ ﯾﺮﻣ ﻲﻜ "- AND رﻮﺼﻟﺎﺑ - AND ﻮﺻ ر - AND ﻮﺻ ر ه - AND ﻮﺻ ر ة - AND (" او ﻟ ﺒ ﺖﯿ ﺑﻷا ﺾﯿ " OR " ﺎﻜﯾﺮﻣا ا ﺔﯿﻨﯿﺗﻼﻟ "- AND " ﺎﻜﯾﺮﻣأ ا ﺔﯿﻨﯿﺗﻼﻟ "- AND " رﻻود ا ﻣ ﻲﻛﺮﯿ "- AND " رﻻود أ ﻣ ﻲﻛﺮﯿ "- AND " رﻻود ا ﯾﺮﻣ ﻲﻜ " ﺎﻛﺮﯿﻣأ "- AND "ا ﺮﻣ ﯾ ﻜ ﺎ ا ﻟ ﻨﺠ ﻮ ﺑ ﯿ ﺔ "- AND " ﺎﻜﯾﺮﻣأ ا ﺔﯿﺑﻮﻨﺠﻟ "- AND " ﺎﻛﺮﯿﻣا ا ﺔﯿﻨﯿﺗﻼﻟ "- AND " ﺎﻛﺮﯿﻣأ ا ﺔﯿﻨﯿﺗﻼﻟ "- AND AND " ﺎﻛﺮﯿﻣأ ا ﻰﻄﺳﻮﻟ "- AND " ﺎﻜﯾﺮﻣا ا ﻰﻄﺳﻮﻟ "- AND " ﺎﻜﯾﺮﻣأ ا ﻰﻄﺳﻮﻟ "- AND " ﺎﻛﺮﯿﻣا ا ﺔﯿﺑﻮﻨﺠﻟ "- AND " ﺔﯿﺑﻮﻨﺠﻟا " ﺔﻔﻀﻟا ﺔﯿﺑﺮﻐﻟا "- AND " ﮫﻘﻄﻨﻤﻟا ﮫﯿﺑﺮﻐﻟا "- AND " ﺔﻘﻄﻨﻤﻟا ﺔﯿﺑﺮﻐﻟا "- AND " ﻦﻄﻨﺷاو ﺖﺳﻮﺑ "- AND " ﺎﻛﺮﯿﻣا ا ﻰﻄﺳﻮﻟ "- " ﻞﺣﺎﺴﻟا ﻲﺑﺮﻐﻟا "- AND " ﻲﺤﻟا ﻲﺑﺮﻐﻟا "- AND " ﺔﻌﻣﺎﺠﻟا ﺔﯿﻜﯾﺮﻣﻷا "- AND " ﺔﻔﻀﻟا ﮫﯿﺑﺮﻐﻟا "- AND 3.2.2 Example Training Texts 3.2.2.1 Negative Political (161 posts)

ﺎﻜﯾﺮﻣأ ﻢﻋﺪﺗ ﻞﯿﺋاﺮﺳإ ﺎﺑ ةﺮﯿﺧﺬﻟ ﺰﮭﺠﺘﻟ ﻰﻠﻋ ﺎﻣ ﻰﻘﺒﺗ ﻦﻣ -America supports Israel with weapons to pre …ﻦﯿﻤﻠﺴﻤﻟا اﺬھ ﻮھ ﻒﯾز ﺔﯿطاﺮﻘﻣﺪﻟا و pare against what is left of the Muslims… this ا# ﺪﻌﻟ ا ‼…ﺔﯿﻛﺮﯿﻣﻷا_ﺔﻟ ﺗ_ةﺰﻏ# ﻘ موﺎ is the sham that is American democracy and #justice…!! #Gaza resists

6 زﺮﺑأ ﻦﯾرﺎﯿﺧ يﻷ )مﺎﻈﻧ( ﺔﯾﺎﻤﺤﻟ ﮫﻨﻣأ :هراﺮﻘﺘﺳاو ﻒﻟﺎﺤﺘﻟا ﻊﻣ -The two prominent options available to any re أﻣﺮﯾﻜﺎ، أو اﻟﺘﺤﺎﻟﻒ ﻣﻊ اﻟﺸﻌﺐ. أﻣﺮﯾﻜﺎ ُﺟﺮﺑﺖ..ﺗﺨﻮن وﺗﻐﺪر gime to protect its safety and stability: allying . ﻒﻟﺎﺤﺘﻟا ﻊﻣ ﺐﻌﺸﻟا ﻢﻟ ﯾ .بﺮﺠ .with America or allying with the people America has been tried… it betrays and de- ceives. Allying with the people has not yet been tried. اﺬﻜھ يﺮﺘﺸﺗ ﺎﻜﯾﺮﻣأ ﻗﺰﺗﺮﻣ ﺎﮭﺘ ﻢﮭﯾﺮﻐﺗو هﺬﮭﺑ لاﻮﻣﻷا ﻟ ﺘ ﻨ ﺬﻔ ,In this manner America pays its mercenaries رﻏﺒﺎﺗﮭﺎ و ﻟﻜﻲ ﯾﻘﺎﺗﻠﻮن اﻟﻤﺠﺎھﺪﯾﻦ ﺑﺪﻻً ﻋﻨﮭﺎ #اﻟﺼ ﻮﺤ تا tempting them with this money to carry out its desires and to kill Jihadis instead of it #The_Awakening 3.2.2.2 Negative Social (33 posts) ﻦﯿﻋوﺪﺨﻤﻠﻟ ﻦﻣ يوﺬﻟا تﺎﻓﺎﻘﺜﻟا ﺔﯿﺑﺮﻐﻟا بﺎﺤﺻأو تﺎھﺎﺠﺗﻻا To those who have been deceived by those .ﺔﻓﺮﺤﻨﻤﻟا تارﺎﻣﻹا# ﺔﯿﻧﺎﻤﻠﻌﻟا# ﺔﯿﻣﺪﻟا# _ﺲﯿﺘﻟا# ﻲﻟﺎﻣﻮﺼﻟا from Western cultures, possessors of crooked ways. #The_Emirates #Secularism ﺎﻣاﺬھ اﺪﯾﺮﯾ اءاﺪﻋأةﺮﻔﻜﻟ ﻦﯾﺪﻟ ﻧذأو ﺎ ﻢﮭﺑ ﻟ اﺮﺒﯿ ﻟ ﺎﻤﻠﻋوﺔﯿ ﻦﻣوﺔﯿﻧ ﺞﮭﻧ -This is what the infidels want—they, the ene ﻢﮭﺠﮭﻧ ﻲﻓ ادﻼﺑ ﻦﯿﻤﻠﺴﻤﻟ ﻰﻠﻋ ﻰﻄﺧ ﺑآ ﺎ ﻢﮭﺋ م بﺮﻐﻟا mies of religion, their ilk the liberals and the قﺮﺸﻟاوﺮﻓﺎﻜﻟا ﺪﺤﻠﻤﻟا -secularists, and those who follow their foot steps, both those in Muslim countries who do it just like their parents and [those in] the infi- del West and the atheist East. 3.2.2.3 Neutral Political (254 posts) RT @B_Eltaweal ةرازو ا ﻓﺪﻟ :عﺎ ﻦﻟ ﯾ ﻢﺘ نﻼﻋإ ﺎﺣ ﺔﻟ The [US] Department of State: We will not be ئراﻮﻄﻟا ﻲﻓ ﺮﻘﻣ ..نﻮﺟﺎﺘﻨﺒﻟا ﻊﺑﺎﺘﻧو ﻦﻋ بﺮﻗ ﺎﻣ ثﺪﺤﯾ ﻲﻓ -announcing a state of emergency in the Penta ﺰﻛﺮﻣ ﻗ ﯿ ﺎ د ة ا ﻟ ﯾﺮﺤﺒ ﺔ ﻲﻓ و# ا ﻦﻄﻨﺷ -gon, and we are closely watching what’s hap pening in the Naval Headquarters in DC #Libya ﺶﻋاد ﻲﻟﻮﺘﺴﯾ ﻲﻓ قاﺮﻌﻟا ﻰﻠﻋ ﺔﻨﺤﺷ ﺦﯾراﻮﺻ ISIS has gained possession of a container of :ﺔﯿﻜﯾﺮﻣا ﺶﻋاد ﻲﻟﻮﺘﺴﯾ ﻲﻓ ا قاﺮﻌﻟ ﻰﻠﻋ ﺔﻨﺤﺷ ﺦﯾراﻮﺻ American missiles in Iraq: ISIS has gained ...بﺎﺘﻛﺔﯿﻜﯾﺮﻣا #possession of a container of American missiles Egypt in Iraq, by… #Libya #Egypt ﺒﺧا# رﺎ ﺎﻛو ﺔﻟ ﺔﯿﻜﯾﺮﻣأ ﻒﺸﻜﺗ ﺗ ﻔ ﻞﯿﺻﺎ ﺔﻘﻔﺻ أ ﺔﻤﻈﻧ ا اﺮﻤﻟ ﻗ ﺔﺒ News An American agency has revealed the# ﻦﯿﺑ ﻦﻄﻨﺷاو ةﺮھﺎﻘﻟاو -details of a joint American-Egyptian agree ment regarding monitoring systems 3.2.2.4 Neutral Social (64 posts) تﻼﺴﻠﺴﻣ ﻓﻮﻣ ﺰﯿ بﻮﺑ نرﻮﻛ : ﯾ ﺪﺒ أ ا ﻟ مﻮﯿ ضﺮﻋ ا ﻠﺤﻟ ﺔﻘ ﻰﻟوﻻا Popcorn Movies” series: Today is the airing“ ﻦﻣ ا ﻟ ﻢﺳﻮﻤ ا ﻟ ﺜ ﺎ ﻦﻣ ﻦﻣ ا ﻟ ﺴﻤ ﻠ ﻞﺴ of E1.S8 of the series “The Big Bang Theory” The Big Bang Theory ﻲﻓ ا ﯾﺮﻣ ﻜ ﺎ ﻰﻠﻋ ﻗ ﻨ ﺎ ة in America on the CBS channel CBS لوأ ﺑ ﺮﺌ ﺑ لوﺮﺘ ﺮﻔﺣ ﻲﻓ ا ﺎﻌﻟ ﻢﻟ ﺮﻔﺣ ﻲﻓ ا تﺎﯾﻻﻮﻟ ا ةﺪﺤﺘﻤﻟ The first oil well in the world was dug in the ﯿﻜﯾﺮﻣﻻا ﺔ ﺎﻋ م 1859 .ىدﻼﯿﻣ USA in 1859 ﺖﻠﺻﻮﺗ ﺔﺳارد ﺔﯿﻜﯾﺮﻣأ إ ﻰﻟ نأ ا ﺎﺠﺴﻟ ﺮﺋ ﺔﯿﻧوﺮﺘﻜﻟﻹا يﻮﺘﺤﺗ An American study concluded that e-cigarettes ﻰﻠﻋ ﺾﻌﺑ ا داﻮﻤﻟ ا ﺔﯾوﺎﻤﯿﻜﻟ ا ،ﺔﻣﺎﺴﻟ ﺎﻤﻛ أ ﺎﮭﻧ ﻟ ﺖﺴﯿ ﻼﯾﺪﺑ ﻨﻣآ ﺎ -contain a number of poisonous chemical ele ﻦﻋ ﻦﯿﺧﺪﺗ ﺮﺋﺎﺠﺳ ا ﻎﺒﺘﻟ ا ﺔﯾدﺎﻌﻟ ments and that they are not a safe substitute for smoking regular tobacco cigarettes

7 3.2.2.5 Positive (18 posts) ﻮﻠﺤﻟا دﻼﺒﺑ بﺮﻐﻟا ﻮﻧإ ﺔﺒﺴﻧ ةﺮﯿﺒﻛ ﻦﻣ ﺐﻌﺸﻟا ﺶﻣ ﺶﯾﺎﻋ What’s great about Western countries is that a .لﻮﮭﺠﻤﻟﺎﺑ ﻢﮭﺗﺎﯿﺣ.. ﺔﻤﻈﻨﻣ ﺮﯿﺘﻛ -large portion of the people don’t live in igno rance… their lives are very organized ﻲﻧﺎﻤﻠﻌﻟا ﻲﺑﺮﻌﻟا ﺚﺤﺒﯾ ﻦﻋ ﺮﻤﺨﻟا ءﺎﺴﻨﻟاو ﺔﺑرﺎﺤﻣو ﻦﯾﺪﻟا ، Secular Arab people get into alcohol and girls ﻲﻧﺎﻤﻠﻌﻟا ﻲﺑﺮﻐﻟا ﺚﺤﺒﯾ ﻦﻋ رﻮﻄﺘﻟا ةاوﺎﺴﻣو نﺎﯾدﻷا ةﺎﯿﺣو and fighting religion; secular Westerners, on اﻹﻧﺴﺎن وﻓﻘﺎً ﻟﻠﻘﺎﻧﻮن the other hand, get into development and equality for religions and living in accordance with the law بﺮﻐﻟا اﻮﺴﯿﻟ ةﺮﻗﺎﺒﻋ ﻦﺤﻧو ﺎﻨﺴﻟ ءﺎﯿﺒﻏأ ، ﻢھ ﻂﻘﻓ نﻮﻤﻋﺪﯾ People in the West aren’t geniuses, and we ﻞﺷﺎﻔﻟا ﻰﺘﺣ ﺢﺠﻨﯾ ﻦﺤﻧو برﺎﺤﻧ ﺢﺟﺎﻨﻟا ﻰﺘﺣ ﻞﺸﻔﯾ aren’t idiots—it’s just that they support those who are failing until they succeed, while we attack those who are successful until they fail 3.2.2.6 Irrelevant (223 posts) ﻓو ﺎ ة ﺐﻋﻻ ا ﺔﻠﺴﻟ ﻲﻜﯾﺮﻣﻷا ا ﺎﺴﻟ ﻖﺑ ﺲﯿﺳﻮﻣ ﺎﻣ نﻮﻟ ا ﺪﯾﺰﻤﻟ ﻦﻣ The former American basketball player Moses ﻞﯿﺻﺎﻔﺘﻟا ﻰﻠﻋ Malone has passed away—for more details see this link 15 ﻠﻣ نﻮﯿ ﻊﺑﺎﺘﻣ ﻟ ﺎﺑﺎﺒ ا ﻟ نﺎﻜﯿﺗﺎﻔ ﻰﻠﻋ "ﺮﺘﯾﻮﺗ" لﻼﺧ ﮫﺗرﺎﯾز million followers for the Catholic pope on 15 بﻮﻨﺠﻟ أ ﺎﻜﯾﺮﻣ Twitter during his visit to South America ﺪھﺎﺷ ﻢﺠﺣ ا ﻟ ﻨ كﺰﯿ ﻘﻣ ﺔﻧرﺎ ﯾﺪﻤﺑ ﺔﻨ سﻮﻟ ا سﻮﻠﺠﻧ ﺔﯿﻜﯾﺮﻣﻷا . !!. Look at the size of this meteor compared to the نﺎﺤﺒﺳ ﷲ !!American city of Los Angelos – Wow 3.3 ISIS Analysis

621 posts total. 3.3.1 Keywords ﮫﯿﺸﻋاد OR ﺔﯿﺸﻋاﺪﻟا OR ﺔﯿﺸﻋاد OR ﻲﺸﻋاﺪﻟا OR ﻲﺸﻋاد OR دو ﺶﻋا OR ﺶﻋاﺪﻟ OR ﺶﻋاﺪﻓ OR ﺶﻋاﺪﺑ OR ﺶﻋاد OR ﺶﻋاوﺪﻟﺎﺑ OR ﺶﻋاوﺪﻟا OR ودو ﺶﻋا OR ﺶﻋاوﺪﻟ OR ﺶﻋاوﺪﻓ OR ﺶﻋاوﺪﺑ OR ود ﺶﻋا OR ﮫﯿﺸﻋاﺪﻟا OR OR " ﺔﻟوﺪﻟﺎﻓ ﺔﯿﻣﻼﺳﻹا " OR " ﺔﻟوﺪﻟﺎﺑ ﺔﯿﻣﻼﺳﻹا " OR " ﺔﻟوﺪﻟا ﺔﯿﻣﻼﺳﻹا " OR او ﺶﻋاوﺪﻟ OR ﺶﻋاوﺪﻠﻟ OR ﺶﻋاوﺪﻟﺎﻓ OR " ﺔﻟوﺪﻟﺎﻓ ﺔﯿﻣﻼﺳﻻا " OR " ﺔﻟوﺪﻟﺎﺑ ﺔﯿﻣﻼﺳﻻا " OR " ﺔﻟوﺪﻟا ﺔﯿﻣﻼﺳﻻا " OR " او ﺔﻟوﺪﻟ ﺔﯿﻣﻼﺳﻹا " OR " ﺔﻟوﺪﻠﻟ ﺔﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻟﺎﻓ ﮫﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻟﺎﺑ ﮫﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻟا ﮫﯿﻣﻼﺳﻹا " OR " او ﺔﻟوﺪﻟ ﺔﯿﻣﻼﺳﻻا " OR " ﺔﻟوﺪﻠﻟ ﺔﯿﻣﻼﺳﻻا " OR " ﮫﻟوﺪﻟﺎﻓ ﮫﯿﻣﻼﺳﻻا " OR " ﮫﻟوﺪﻟﺎﺑ ﮫﯿﻣﻼﺳﻻا " OR " ﮫﻟوﺪﻟا ﮫﯿﻣﻼﺳﻻا " OR " او ﮫﻟوﺪﻟ ﮫﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻠﻟ ﮫﯿﻣﻼﺳﻹا " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟﺎﻓ " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟﺎﺑ " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟا " OR " او ﮫﻟوﺪﻟ ﯿﻣﻼﺳﻻا ﮫ " OR " ﮫﻟوﺪﻠﻟ ﮫﯿﻣﻼﺳﻻا " ﺗو ﻢﯿﻈﻨ " OR " ﻢﯿﻈﻨﺘﻟ ﺔﻟوﺪﻟا " OR " ﻢﯿﻈﻨﺘﺑ ﺔﻟوﺪﻟا " OR " ﻢﯿﻈﻨﺗ ﺔﻟوﺪﻟا " OR " او ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟ " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻠﻟ " ﻢﯿﻈﻨﺘﻓ " OR " ﺗو ﻢﯿﻈﻨ ا ﻟ ﮫﻟوﺪ " OR " ﻢﯿﻈﻨﺘﻟ ﮫﻟوﺪﻟا " OR " ﻢﯿﻈﻨﺘﺑ ﮫﻟوﺪﻟا " OR " ﻢﯿﻈﻨﺗ ﮫﻟوﺪﻟا " OR " ﻢﯿﻈﻨﺘﻓ ﺔﻟوﺪﻟا " OR " ﺔﻟوﺪﻟا OR " ﺔﻟوﺪﻓ ا ﺔﻓﻼﺨﻟ " OR " ودو ﻟ ﺔ ا ﺔﻓﻼﺨﻟ " OR " ﺔﻟوﺪﻟ ا ﺔﻓﻼﺨﻟ " OR " ﺔﻟوﺪﺑ ا ﺔﻓﻼﺨﻟ " OR " ود ﻟ ﺔ ا ﺔﻓﻼﺨﻟ " OR " ﮫﻟوﺪﻟا ﺧﻷ ﺒ ﺎ ر " OR " رﺎﺒﺧﺄﺑ ﺔﻓﻼﺨﻟا " OR " ﺒﺧأ رﺎ ا ﺔﻓﻼﺨﻟ " OR او ﻟ يداﺪﻐﺒ OR يداﺪﻐﺒﻟﺎﻓ OR يداﺪﻐﺒﻠﻟ OR يداﺪﻐﺒﻟﺎﺑ OR يداﺪﻐﺒﻟا " رﺎﺒﺧﺄﻓ ﺔﻓﻼﺨﻟا " OR " رﺎﺒﺧأو ا ﺔﻓﻼﺨﻟ " OR " ﺔﻓﻼﺨﻟا

8 3.3.2 Example Training Texts 3.3.2.1 Negative (175 posts) د# ﺶﻋا ﻢھ ﻧ ﻢﮭﺴﻔ ا ﻟ ﺎﺨﻤ ﺮﺑ تا ﯾﺮﻣﻹا ﻜ ﯿ ﮫ كوﺪﻋ_فﺮﻋأ# -ISIS themselves are from the American intel# او هرﻮﺼﻟ ﺗ ثﺪﺤﺘ ligence agencies – the picture speaks for itself #know_your_enemy ﻲﻓ ا ﺖﻗﻮﻟ ا ىﺬﻟ ﯾ ﻞﻐﻠﻐﺘ ﺮﻜﻓ ﺶﻋاد# ﺒﺸﺑ ﺎ ﺑ ﻨ ﺎ اﺮﺳإو ﺋ ﻞﯿ ﻖﻠﻐﺗ This while ISIS’s thinking is spreading among ﺪﺠﺴﻤﻟا ﻞﻛ؛ﻰﺼﻗﻷا مﻮﯾ ﺎﻨﺌﺟﺎﻔﯾ ﺾﻌﺑ ﻦﻣ ءﺎﻤﻠﻋ ﺔﻣﻷا our youth and Israel’s closing the al-Aqsa !ﻢﮭﺗﺎﯾﻮﻟوﺄﺑ ﻰﺘﺣ ﻢﺘﻧأ ﻠﻐﺷأ ا"ﻢﻜﺘ !"ةرﻮﻜﻟ -mosque; every day I’m shocked at the priori ties of the national religious leadership! Even you, soccer’s all you care about! ﺐﺠﯾ ﻊﻤﺟ ﺮﺸﻧو رزﺎﺠﻣ د ﺶﻋا ﻖﺤﺑ ﻞھا ا ﺔﻨﺴﻟ ﺑو ﯿ ﺎ ﺎﮭﻧ ﻟ يأﺮﻠ Accounts of ISIS’s atrocities against Sunnis مﺎﻌﻟا ﻲﻨﺴﻟا ﻊﻓدو ﺶﯾﻮﺸﺘﻟا ﻲﺸﻋاﺪﻟا ﺎﮭﯿﻠﻋ ﺎﻤﻛ نﻮﻠﻌﻔﯾ ﺎﻣﺪﻨﻋ -must be collected and publicized – the atroci نﻮﻠﻌﺠﯾ ا ﺔﺒﺤﻟ ﻗ !ﺔﺒ ا_ةرﺰﺠﻣ# ﻟ ﺮﻤﻧﻮﺒ ties must be explained to the Sunni public, and the reality of ISIS’s derangement must be forced upon them, just like what happens when someone makes a mountain out of a molehill! #Albu_Nimr_Massacre 3.3.2.2 Neutral (359 posts) ﻘﻣ ﺎ تﻼﺗ ﺔﯿﺴﻧﺮﻓ بﺮﻀﺗ ﻞﻘﻌﻣ ﺗ ﻢﯿﻈﻨ ”ﺶﻋاد“ ﻲﻓ ا ﺔﻗﺮﻟ French fighters bombard ISIS’s bases in ﯾرﻮﺴﺑ ﺎ Raqqa, Syria لوﻻ ﺮﻣ ة ﻗ ﻨ ﺎ ة 24 ﻮﻌﺳ يد ﺗ ﺚﺒ ﯾﻮﺼﺗ ﻤﻋﺮ ﻠ ﯿ تﺎ ا ﻟ ﻘ ﺾﺒ ﻰﻠﻋ For the first time, Saudi Channel 24 broadcasts ﺎھرا ﺑ ﯿ ﻦﯿ د ﺶﻋا ﺑ ﺎ ﺔﯾدﻮﻌﺴﻟ ا_دﻼﺑ_ءﺎﺴﻧ_فﺎﻄﺘﺧا# ﻦﯿﻣﺮﺤﻟ footage of operations to capture ISIS terrorists ا# ﻟا_رﺎﻌﺴﻧ ﺪ ﻠﺧا ﺔﯿ -in Saudi Arabia #Kidnap pings_of_Women_in_Saudi 3.3.2.3 Positive (54 posts) ﺣﻘﻘﺖ اﻟﺪوﻟﺔ اﻹﺳﻼﻣﯿﺔ ُﺣﻠﻢ اﻟﻤﺴﻠﻤﯿﻦ ﺑﺈﻋﻼن اﻟ ِﺨﻼﻓﺔ. ﻓﺎﻟﻠﮭﻢ ISIS has achieved the dream of all Muslims in ﻚﻟ ا ﺪﻤﺤﻟ ﻦﻣ ﻗ ﻞﺒ ﻦﻣو .ﺪﻌﺑ announcing the caliphate. Praise to God now and forevermore. ﺎﻋ ا ا ا ﻞﺟا ﻨﻋ ﺮﺻﺎ ﺔﮭﺒﺟ ا ﻟ ةﺮﺼﻨ ﻲﻓ ذ ﯾ ﺒ نﺎ ﻒﯾﺮﺑ د ﺮﯾ ا روﺰﻟ Breaking: Members of Al-Nusra Front in ﻠﻌﯾ نﻮﻨ ا ﻟ ﺒ ﺔﻌﯿ ﻟ ﺔﻟوﺪﻠ ﺔﯿﻣﻼﺳﻻا ا ﻟ ﻢﮭﻠ يﺪھا ﺢﻠﺻاو ا ﻊﯿﻤﺠﻟ و Diban, on the outskirts of Deir ez-Zor, are ﺪﺣو ا ﻔﺼﻟ فﻮ -pledging themselves to ISIS – guide and cor rect us, oh God, and unite the ranks ﻰﻟا ﻞﻛ ةﻮﺧﻻا تاﻮﺧﻻاو فﻮﺳ ﻞﻤﻌﻧ ﻰﻠﻋ ﻢﻋد ا رﺎﺼﻧ ا ﺔﻟوﺪﻟ To all our brothers and sisters: We will support ﯿﻣﻼﺳﻻا ﺔ فﻮﺳ ﻞﻤﻌﻧ ﻰﻠﻋ قﻼﻏا ﺎﺴﺣ ﺑ تﺎ ا ﻟ ﯿﺴﻤ ﺌ ﻦﯿ ﻦﻣ our ISIS stalwarts and we will fight to close the ﺔﯿﺟﻮﺤﺼﻟا ﺖﯾﻮﺗر ﺮﺸﻧ -accounts of those who disparage “the Awaken ing” – retweet and share 3.3.2.4 Irrelevant (33 posts) ﻊﯿﺑ ﺖﯾﻮﺗر ﻦﯿﻌﺑﺎﺘﻣو ﻦﯿﺠﯿﻠﺧ ءﺎﻋد# حﻮﺑ# ررد# ﻲﺑد# Selling retweets and followers from the gulf ﺣ# ﻔ ﺮ ا_ ﻟ ﺒ ﻦطﺎ ﺶﻋاد# ﻢﻜﺣ# ﻘﺣ# ﯿ ﮫﻘ ﺎﺣ# ﻞﺋ اﺮﻐﺘﺴﻧإ# م [series of unrelated hashtags] ﺑ# ﺖﯾﻮﺗر_ﻊﯿ ﺑ# ﺘﻣ_ﻊﯿ ﺎ ﻦﯿﻌﺑ

9 رﻮﺻ ﺒﺷ بﺎ ﺑ ﻨ تﺎ ﯾﻮﺗ ﺮﺘ ﻮﻟﻮﻓ ا ﯾﺮﻟ ضﺎ ا مﺰﺤﻟ ﺶﻋاد ﯿﻜﻣ جﺎ …Pictures youth girls Twitter follow Riyadh نﺎﻀﻣر ﻞﺴﻠﺴﻣ ﺎﺤﺗﻻا د ا ﻟ لﻼﮭ ا ﻟ ﺮﺼﻨ ﻲﻠھﻻا ﺪھا فا ﻨﺳ بﺎ […additional words unrelated to each other] تﺎﺷ ﻓ ﯿ ﺪ ﻮﯾ http://t.co/skqN224ibM 261 زو ﯾ ﺮ ﺟ ﮓﻨ د ا ﺶﻋ رد ﻞﺻﻮﻣ ﺑ ﮫ ﺖﮐﻼھ ﯿﺳر ﺪ ﺎﺧ# ﯽﺟر [something in Farsi] _ﺮﻋ# ا ق # د ا ﺶﻋ http://t.co/rCLCj0wsVR 3.4 Combined Analysis

152 posts total. 3.4.1 Keywords ﺎﻜﯾﺮﻣﺎﺑ OR ﺎﻜﯾﺮﻣﺄﺑ OR ﻣﻻ ﯿ ﻛﺮ ﺎ OR ﻣﻷ ﯿ ﻛﺮ ﺎ OR ﺮﻣﻻ ﯾ ﻜ ﺎ OR ﺮﻣﻷ ﯾ ﻜ ﺎ OR ﺎﻛﺮﯿﻣا OR ﺎﻛﺮﯿﻣأ OR ﺎﻜﯾﺮﻣا OR ﺎﻜﯾﺮﻣأ ) OR ﺎﻛﺮﯿﻣأو OR ﺎﻜﯾﺮﻣاو OR ﺎﻜﯾﺮﻣأو OR ﺎﻛﺮﯿﻣﺎﻓ OR ﺎﻛﺮﯿﻣﺄﻓ OR ﺎﻜﯾﺮﻣﺎﻓ OR ﺎﻜﯾﺮﻣﺄﻓ OR ﺎﻛﺮﯿﻣﺎﺑ OR ﺎﻛﺮﯿﻣﺄﺑ OR ﻲﻛﺮﯿﻣﻻا OR ﻲﻛﺮﯿﻣﻷا OR ﻲﻛﺮﯿﻣا OR ﻲﻛﺮﯿﻣأ OR ﻲﻜﯾﺮﻣﻻا OR ﻲﻜﯾﺮﻣﻷا OR ﻲﻜﯾﺮﻣا OR ﻲﻜﯾﺮﻣأ OR ﺎﻛﺮﯿﻣاو OR ﻲﻛﺮﯿﻣﻼﻟ OR ﻲﻛﺮﯿﻣﻸﻟ OR ﻣﻻ ﯿ ﻲﻛﺮ OR ﻣﻷ ﯿ ﻲﻛﺮ OR ﯾﺮﻣﻼﻟ ﻲﻜ OR ﯾﺮﻣﻸﻟ ﻲﻜ OR ﺮﻣﻻ ﯾ ﻲﻜ OR ﺮﻣﻷ ﯾ ﻲﻜ OR OR ﻲﻛﺮﯿﻣﻻﺎﺑ OR ﯿﻣﻷﺎﺑ ﻲﻛﺮ OR ﻲﻛﺮﯿﻣﺎﺑ OR ﻲﻛﺮﯿﻣﺄﺑ OR ﻲﻜﯾﺮﻣﻻﺎﺑ OR ﻲﻜﯾﺮﻣﻷﺎﺑ OR ﻲﻜﯾﺮﻣﺎﺑ OR ﻲﻜﯾﺮﻣﺄﺑ OR ﻲﻛﺮﯿﻣﻻﺎﻓ OR ﻲﻛﺮﯿﻣﻷﺎﻓ OR ﻲﻛﺮﯿﻣﺎﻓ OR ﻲﻛﺮﯿﻣﺄﻓ OR ﻲﻜﯾﺮﻣﻻﺎﻓ OR ﻲﻜﯾﺮﻣﻷﺎﻓ OR ﻲﻜﯾﺮﻣﺎﻓ OR ﻲﻜﯾﺮﻣﺄﻓ OR ﻲﻛﺮﯿﻣﻻاو OR ﻲﻛﺮﯿﻣﻷاو OR ﻲﻛﺮﯿﻣاو OR ﻲﻛﺮﯿﻣأو OR ﻲﻜﯾﺮﻣﻻاو OR ﻲﻜﯾﺮﻣﻷاو OR ﻲﻜﯾﺮﻣاو OR ﻲﻜﯾﺮﻣأو ﺮﻣﻷ ﯾ ﻜ ﯿ ﺔ OR ﯿﻛﺮﯿﻣﻻا ﺔ OR ﯿﻛﺮﯿﻣﻷا ﺔ OR ﺔﯿﻛﺮﯿﻣا OR ﺔﯿﻛﺮﯿﻣأ OR ﯿﻜﯾﺮﻣﻻا ﺔ OR ﯿﻜﯾﺮﻣﻷا ﺔ OR ﺔﯿﻜﯾﺮﻣا OR ﺔﯿﻜﯾﺮﻣأ OR ﺔﯿﻜﯾﺮﻣﺄﺑ OR ﻛﺮﯿﻣﻼﻟ ﯿ ﺔ OR ﻛﺮﯿﻣﻸﻟ ﯿ ﺔ OR ﻣﻻ ﯿ ﻛﺮ ﯿ ﺔ OR ﻣﻷ ﯿ ﻛﺮ ﯿ ﺔ OR ﯾﺮﻣﻼﻟ ﻜ ﯿ ﺔ OR ﯾﺮﻣﻸﻟ ﻜ ﯿ ﺔ OR ﺮﻣﻻ ﯾ ﻜ ﯿ ﺔ OR OR ﺔﯿﻜﯾﺮﻣﺄﻓ OR ﻣﻻﺎﺑ ﺔﯿﻛﺮﯿ OR ﺔﯿﻛﺮﯿﻣﻷﺎﺑ OR ﺔﯿﻛﺮﯿﻣﺎﺑ OR ﺔﯿﻛﺮﯿﻣﺄﺑ OR ﺔﯿﻜﯾﺮﻣﻻﺎﺑ OR ﺔﯿﻜﯾﺮﻣﻷﺎﺑ OR ﺔﯿﻜﯾﺮﻣﺎﺑ OR ﺔﯿﻜﯾﺮﻣأو OR ﺔﯿﻛﺮﯿﻣﻻﺎﻓ OR ﺔﯿﻛﺮﯿﻣﻷﺎﻓ OR ﺔﯿﻛﺮﯿﻣﺎﻓ OR ﺔﯿﻛﺮﯿﻣﺄﻓ OR ﺔﯿﻜﯾﺮﻣﻻﺎﻓ OR ﺔﯿﻜﯾﺮﻣﻷﺎﻓ OR ﺔﯿﻜﯾﺮﻣﺎﻓ OR ﻦﯿﯿﻜﯾﺮﻣأ OR ﺔﯿﻛﺮﯿﻣﻻاو OR ﺔﯿﻛﺮﯿﻣﻷاو OR ﺔﯿﻛﺮﯿﻣاو OR ﺔﯿﻛﺮﯿﻣأو OR ﺔﯿﻜﯾﺮﻣﻻاو OR ﺔﯿﻜﯾﺮﻣﻷاو OR ﺔﯿﻜﯾﺮﻣاو OR ﺮﻣﻷ ﯾ ﻜ ﯿ ﯿ ﻦ OR ﯿﻛﺮﯿﻣﻻا ﻦﯿ OR ﯿﻛﺮﯿﻣﻷا ﻦﯿ OR ﻦﯿﯿﻛﺮﯿﻣا OR ﻦﯿﯿﻛﺮﯿﻣأ OR ا ﺮﻣﻻ ﯾ ﻜ ﯿ ﯿ ﻦ OR ﯿﻜﯾﺮﻣﻷا ﻦﯿ OR ﻦﯿﯿﻜﯾﺮﻣا ﻦﯿﯿﻜﯾﺮﻣﺄﺑ OR ﻛﺮﯿﻣﻼﻟ ﯿ ﻦﯿ OR ﻛﺮﯿﻣﻸﻟ ﯿ ﻦﯿ OR ﻣﻻ ﯿ ﻛﺮ ﯿ ﯿ ﻦ OR ﻣﻷ ﯿ ﻛﺮ ﯿ ﯿ ﻦ OR ﯾﺮﻣﻼﻟ ﻜ ﯿ ﻦﯿ OR ﯾﺮﻣﻸﻟ ﻜ ﯿ ﻦﯿ OR ﺮﻣﻻ ﯾ ﻜ ﯿ ﯿ ﻦ ﻣ OR ﻦﯿﯿﻛﺮﯿﻣﻻﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﻷﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﺄﺑ OR ﻦﯿﯿﻜﯾﺮﻣﻻﺎﺑ OR ﻦﯿﯿﻜﯾﺮﻣﻷﺎﺑ OR ﻦﯿﯿﻜﯾﺮﻣﺎﺑ OR ﻦﯿﯿﻛﺮﯿﻣﻻﺎﻓ OR ﻦﯿﯿﻛﺮﯿﻣﻷﺎﻓ OR ﻦﯿﯿﻛﺮﯿﻣﺎﻓ OR ﻦﯿﯿﻛﺮﯿﻣﺄﻓ OR ﻦﯿﯿﻜﯾﺮﻣﻻﺎﻓ OR ﻦﯿﯿﻜﯾﺮﻣﻷﺎﻓ OR ﻦﯿﯿﻜﯾﺮﻣﺎﻓ OR ﻦﯿﯿﻜﯾﺮﻣﺄﻓ OR ﯿﻛﺮﯿﻣﻷاو ﻦﯿ OR ﯿﻛﺮﯿﻣاو ﻦﯿ OR ﯿﻛﺮﯿﻣأو ﻦﯿ OR ﯿﻜﯾﺮﻣﻻاو ﻦﯿ OR ﯿﻜﯾﺮﻣﻷاو ﻦﯿ OR ﯿﻜﯾﺮﻣاو ﻦﯿ OR ﯿﻜﯾﺮﻣأو ﻦﯿ OR نﻮﯿﻛﺮﯿﻣﻷا OR نﻮﯿﻛﺮﯿﻣا OR نﻮﯿﻛﺮﯿﻣأ OR نﻮﯿﻜﯾﺮﻣﻻا OR نﻮﯿﻜﯾﺮﻣﻷا OR نﻮﯿﻜﯾﺮﻣا OR نﻮﯿﻜﯾﺮﻣأ OR ﻻاو ﻦﯿﯿﻛﺮﯿﻣ OR ﻣﻻ ﯿ ﻛﺮ ﯿ نﻮ OR ﻣﻷ ﯿ ﻛﺮ ﯿ نﻮ OR ﯾﺮﻣﻼﻟ ﻜ ﯿ نﻮ OR ﯾﺮﻣﻸﻟ ﻜ ﯿ نﻮ OR ﺮﻣﻻ ﯾ ﻜ ﯿ نﻮ OR ﺮﻣﻷ ﯾ ﻜ ﯿ نﻮ OR نﻮﯿﻛﺮﯿﻣﻻا OR OR نﻮﯿﻛﺮﯿﻣﺄﺑ OR نﻮﯿﻜﯾﺮﻣﻻﺎﺑ OR ﯾﺮﻣﻷﺎﺑ نﻮﯿﻜ OR نﻮﯿﻜﯾﺮﻣﺎﺑ OR نﻮﯿﻜﯾﺮﻣﺄﺑ OR ﻛﺮﯿﻣﻼﻟ ﯿ نﻮ OR ﻛﺮﯿﻣﻸﻟ ﯿ نﻮﯿﻛﯿﻸ OR نﻮﯿﻜﯾﺮﻣﻻﺎﻓ OR نﻮﯿﻜﯾﺮﻣﻷﺎﻓ OR نﻮﯿﻜﯾﺮﻣﺎﻓ OR نﻮﯿﻜﯾﺮﻣﺄﻓ OR نﻮﯿﻛﺮﯿﻣﻻﺎﺑ OR نﻮﯿﻛﺮﯿﻣﻷﺎﺑ OR نﻮﯿﻛﺮﯿﻣﺎﺑ OR نﻮﯿﻜﯾﺮﻣﻷاو OR نﻮﯿﻜﯾﺮﻣاو OR نﻮﯿﻜﯾﺮﻣأو OR نﻮﯿﻛﺮﯿﻣﻻﺎﻓ OR نﻮﯿﻛﺮﯿﻣﻷﺎﻓ OR نﻮﯿﻛﺮﯿﻣﺎﻓ OR نﻮﯿﻛﺮﯿﻣﺄﻓ نﺎﻜﯾﺮﻣﻷا OR نﺎﻜﯾﺮﻣا OR نﺎﻜﯾﺮﻣأ OR نﻮﯿﻛﺮﯿﻣﻻاو OR نﻮﯿﻛﺮﯿﻣﻷاو OR نﻮﯿﻛﺮﯿﻣاو OR نﻮﯿﻛﺮﯿﻣأو OR نﻮﯿﻜﯾﺮﻣﻻاو OR ﯾﺮﻣﻸﻟ ﻜ نﺎ OR ﺮﻣﻻ ﯾ ﻜ ﺎ ن OR ﺮﻣﻷ ﯾ ﻜ ﺎ ن OR نﺎﻛﺮﯿﻣﻻا OR نﺎﻛﺮﯿﻣﻷا OR نﺎﻛﺮﯿﻣا OR نﺎﻛﺮﯿﻣأ OR نﺎﻜﯾﺮﻣﻻا OR OR نﺎﻜﯾﺮﻣﻷﺎﺑ OR نﺎﻜﯾﺮﻣﺎﺑ OR ﺮﻣﺄﺑ نﺎﻜﯾ OR ﻛﺮﯿﻣﻼﻟ نﺎ OR ﻛﺮﯿﻣﻸﻟ نﺎ OR ﻣﻻ ﯿ ﻛﺮ ﺎ ن OR ﻣﻷ ﯿ ﻛﺮ ﺎ ن OR ﯾﺮﻣﻼﻟ ﻜ نﺎ ﺮﻼ OR نﺎﻜﯾﺮﻣﻷﺎﻓ OR نﺎﻜﯾﺮﻣﺎﻓ OR نﺎﻜﯾﺮﻣﺄﻓ OR نﺎﻛﺮﯿﻣﻻﺎﺑ OR نﺎﻛﺮﯿﻣﻷﺎﺑ OR نﺎﻛﺮﯿﻣﺎﺑ OR نﺎﻛﺮﯿﻣﺄﺑ OR نﺎﻜﯾﺮﻣﻻﺎﺑ نﺎﻜﯾﺮﻣﻷاو OR نﺎﻜﯾﺮﻣاو OR نﺎﻜﯾﺮﻣأو OR نﺎﻛﺮﯿﻣﻻﺎﻓ OR نﺎﻛﺮﯿﻣﻷﺎﻓ OR نﺎﻛﺮﯿﻣﺎﻓ OR نﺎﻛﺮﯿﻣﺄﻓ OR نﺎﻜﯾﺮﻣﻻﺎﻓ تﺎﯾﻻﻮﻠﻟ " OR " تﺎﯾﻻﻮﻟا ةﺪﺤﺘﻤﻟا " OR نﺎﻛﺮﯿﻣﻻاو OR نﺎﻛﺮﯿﻣﻷاو OR نﺎﻛﺮﯿﻣاو OR نﺎﻛﺮﯿﻣأو OR نﺎﻜﯾﺮﻣﻻاو OR OR ﻦﻄﻨﺷاﻮﻟ OR ﻦﻄﻨﺷاو OR " او تﺎﯾﻻﻮﻟ ا ةﺪﺤﺘﻤﻟ " OR " تﺎﯾﻻﻮﻟﺎﻓ ةﺪﺤﺘﻤﻟا " OR " تﺎﯾﻻﻮﻟﺎﺑ ةﺪﺤﺘﻤﻟا " OR " ةﺪﺤﺘﻤﻟا ﺎﻣﺎﺑوﺄﺑ OR ﺑﻻ ﺎ ﻣ ﺎ OR وﻻ ﺑ ﺎ ﻣ ﺎ OR وﻷ ﺑ ﺎ ﻣ ﺎ OR ﺎﺑا ﺎﻣ OR ﺑوا ﺎﻣﺎ OR ﺑوأ ﺎﻣﺎ OR وو ﻦﻄﻨﺷا OR ﻦﻄﻨﺷاﻮﻓ OR ﻦﻄﻨﺷاﻮﺑ بﺮﻐﻟﺎﺑ OR بﺮﻐﻠﻟ OR بﺮﻐﻟا OR ﺎﻣﺎﺑا OR ﺑوا ﺎﻣﺎ OR ﺑوأ ﺎﻣﺎ OR ﺎﻣﺎﺑا OR ﺑوا ﺎﻣﺎ OR ﺑوأ ﺎﻣﺎ OR ﺎﻣﺎﺑا OR ﺑوا ﺎﻣﺎ OR OR ﻲﺑﺮﻐﻓ OR ﻲﺑﺮﻐﻟﺎﺑ OR ﻲﺑﺮﻐﺑ OR ﻲﺑﺮﻐﻠﻟ OR ﻲﺑﺮﻐﻟ OR ﻲﺑﺮﻐﻟا OR ﺮﻏ ﺑ ﻲ OR او بﺮﻐﻟ OR بﺮﻐﻟﺎﻓ OR ﺑﺮﻐﻓ ﺔﯿ OR ﺔﯿﺑﺮﻐﻟﺎﺑ OR ﺔﯿﺑﺮﻐﺑ OR ﺔﯿﺑﺮﻐﻠﻟ OR ﺔﯿﺑﺮﻐﻟ OR ﺔﯿﺑﺮﻐﻟا OR ﺮﻏ ﺑ ﯿ ﺔ OR او ﻲﺑﺮﻐﻟ OR ﺮﻏو ﻲﺑ OR ﻲﺑﺮﻐﻟﺎﻓ ﺖﯿﺒﻠﻟ " OR " ﺖﯿﺒﻠﻟ ﺾﯿﺑﻻا " OR " ﺖﯿﺒﻟا ﺾﯿﺑﻷا " OR " ﺖﯿﺒﻟا ﺾﯿﺑﻻا " OR او ﺔﯿﺑﺮﻐﻟ OR ﺮﻏو ﺑ ﯿ ﺔ OR ﺔﯿﺑﺮﻐﻟﺎﻓ OR " او ﻟ ﺒ ﺖﯿ ﺾﯿﺑﻻا " OR " ﺖﯿﺒﻟﺎﻓ ﺾﯿﺑﻷا " OR " ﺖﯿﺒﻟﺎﻓ ﺾﯿﺑﻻا " OR " ﺖﯿﺒﻟﺎﺑ ﺾﯿﺑﻷا " OR " ﺖﯿﺒﻟﺎﺑ ﺾﯿﺑﻻا " OR " ﺑﻷا ﺾﯿ ﻷ

10 OR ﻲﺸﻋاﺪﻟا OR ﻲﺸﻋاد OR دو ﺶﻋا OR ﺶﻋاﺪﻟ OR ﺶﻋاﺪﻓ OR ﺶﻋاﺪﺑ OR ﺶﻋاد ) AND (" او ﻟ ﺒ ﺖﯿ ﺾﯿﺑﻷا " OR " ﺔﻟوﺪﻟﺎﻓ ﺔﯿﻣﻼﺳﻹا " OR " ﺔﻟوﺪﻟﺎﺑ ﺔﯿﻣﻼﺳﻹا " OR " ﺔﻟوﺪﻟا ﺔﯿﻣﻼﺳﻹا " OR ﮫﯿﺸﻋاﺪﻟا OR ﮫﯿﺸﻋاد OR ﺔﯿﺸﻋاﺪﻟا OR ﺔﯿﺸﻋاد " ﺔﻟوﺪﻟﺎﻓ ﺔﯿﻣﻼﺳﻻا " OR " ﺔﻟوﺪﻟﺎﺑ ﺔﯿﻣﻼﺳﻻا " OR " ﺔﻟوﺪﻟا ﺔﯿﻣﻼﺳﻻا " OR " او ﺔﻟوﺪﻟ ﺔﯿﻣﻼﺳﻹا " OR " ﺔﻟوﺪﻠﻟ ﺔﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻟﺎﻓ ﮫﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻟﺎﺑ ﮫﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻟا ﮫﯿﻣﻼﺳﻹا " OR " او ﺔﻟوﺪﻟ ﺔﯿﻣﻼﺳﻻا " OR " ﺔﻟوﺪﻠﻟ ﺔﯿﻣﻼﺳﻻا " OR " ﮫﻟوﺪﻟﺎﻓ ﮫﯿﻣﻼﺳﻻا " OR " ﮫﻟوﺪﻟﺎﺑ ﮫﯿﻣﻼﺳﻻا " OR " ﻟوﺪﻟا ﮫ ﮫﯿﻣﻼﺳﻻا " OR " او ﮫﻟوﺪﻟ ﮫﯿﻣﻼﺳﻹا " OR " ﮫﻟوﺪﻠﻟ ﮫﯿﻣﻼﺳﻹا " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟﺎﻓ " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟﺎﺑ " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟا " OR " او ﮫﻟوﺪﻟ ﮫﯿﻣﻼﺳﻻا " OR " ﮫﻟوﺪﻠﻟ ﮫﯿﻣﻼﺳﻻا " OR (" او ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟ " OR " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻠﻟ " OR 3.4.2 Example Training Texts 3.4.2.1 Negative – US & ISIS (28 posts) RT @saleelalmajd1 ﺎﻜﯾﺮﻣأ ﻰﻌﺴﺗ ﻒﯿظﻮﺘﻟ ﺶﻋاد – The US wants to empower ISIS not end it و ﺲﯿﻟ ءﺎﻀﻘﻠﻟ ﺎﮭﯿﻠﻋ ، ﻲﮭﻓ ﻢﻋﺰﺗ ةدﺎﯿﻗ ﻒﻟﺎﺤﺗ ﺎھﺪﺿ ﻢﺛ سرﺎﻤﺗ first the US leads the alliance against ISIS, then ﺎطﻮﻐﺿ ﻰﻠﻋ ا ﻞﺋﺎﺼﻔﻟ ا ﺔﺑﺮﻘﻤﻟ ﺎﮭﻨﻣ ﻲﻓ ﺔﯾرﻮﺳ ﺎﮭﻌﻨﻤﻟ ﻦﻣ لﺎﺘﻗ it exerts pressure on its related factions in Syria ﺶﻋاد ! !to prevent them from fighting ISIS @ManalAlawadhi ﺶﻋاد ﮫﻋﺎﻨﺻ ﮫﯿﻧاﺮﯾا ﺔﯿﻜﯾﺮﻣأو -ISIS is an Iranian and American creation, un ءﺎﻄﻐﺑ ﯾد ﻲﻨ ﻲﻨﺳ لﻮﺻﻮﻠﻟ فاﺪھﻷ ﻂﻄﺨﻤﻟا ﻟا يدﻮﮭﯿ der the veil of Sunni Islam, created to achieve ﻲﻧﻮﯿﮭﺼﻟا ﻢﮭﻠﻟا ﻞﻌﺟا ﻢھﺪﯿﻛ ﻲﻓ ﻢھرﻮﺤﻧ the goals of the Jewish Zionist plotter – oh God may their plans come to naught ﻨﻣ //لﻮﻘ ﺔﺑﺎﺼﻋ ا ﻟ يداﺪﻐﺒ ﺶﻋاد# ﺗ ﻘ ﺎ ﻞﺗ ﻲﻓ ﻒﯾر ﺐﻠﺣ# -Baghdadi’s gang, ISIS, are fighting on the out ﺑﺮﻌﺑ تﺎ ﺔﯿﻜﯾﺮﻣا ﻦﻣ عﻮﻧ ﻲﻔﻣﺎھ ﻢﺗ ﺎھﺪﯾوﺰﺗ ﺎﮭﺑ ﻦﻣ ﻞﺒﻗ skirts of Aleppo using American-made ﺎﻜﯾﺮﻣا# ﻦﻋ ﻖﯾﺮط ا ﺎﻤﻟ ﻲﻜﻟ ﻲﻓ ا# قاﺮﻌﻟ Humvees – they were supplied with them by America, through al-Maliki in #Iraq 3.4.2.2 Negative – US & Syria/Iran/Shia (10 posts) ﺎﻜﯾﺮﻣا ﺪﯾﺮﺗ ﺐﯾرﺪﺗ ا ﺶﯿﺠﻟ ا ﺮﺤﻟ ﺗﺎﻘﻤﻟ ﺔﻠ ﺶﻋاد# ﻲﻓ ﺳ# رﻮ ﯾ ﺎ America wants to train the Syrian Free Army ﺐﯿط ﺎﻈﻧو م ﺪﺳﻻا# ﻦﻣ ﮫﺑرﺎﺤﯿﺳ ؟ ﻲھﺎﻣ ﯾﻮﻟوا تﺎ ا بﺮﻐﻟ ﻲﻓ to fight ISIS in Syria – okay, and what about بﺮﺤﻟا ةﺮﺋاﺪﻟا كﺎﻨھ ﺬﻨﻣ 5 ﻮﻨﺳ تا ؟ ?Asad’s regime, who’s going to fight them What are the West’s priorities in this war that’s been going on now for five years? ﺦﯿﺸﻟا ﺰﯾﺰﻌﻟاﺪﺒﻋ :نازﻮﻔﻟا ﺔﺑﺎﺼﻋ د# ﺶﻋا ﻨﺻ ﺔﻋﺎ Sheikh Abd al-Aziz al-Fouzan: The gang ISIS ﺒﺨﺘﺳا ﺗرﺎ ﺔﯿ ﺔﯿﻜﯾﺮﻣأ إ اﺮﯾ ﻧ ﺔﯿ د# ا_ﻢﺟﺎﮭﺗ_ﻻ_ﺶﻋا ناﺮﯾ -is a creation of the American and Iranian intel ligence agencies – ISIS doesn’t attack Iran تﺎﯾﻻﻮﻟا ةﺪﺤﺘﻤﻟا ﺔﯿﻜﯾﺮﻣﻻا تﺮﯿﻏ ﺎﮭﺗﺎﯾﻮﻟوا ﻲﻓ ﺔﯾرﻮﺳ ﻦﻣ The USA has changed its priorities in Syria ﻘﺳا طﺎ ا ﻟ ﺎﻈﻨ م ا ﻰﻟ ﻘﺳا طﺎ د ﺶﻋا ﺑو ﺎ ﺖﺗ ﻰﻠﻋ ﻚﺷو ﺗ ﺞﯾﻮﺘ ا ناﺮﯾ ,from toppling the regime to getting rid of ISIS ﺔﻟوﺪﻟا ﺔﯿﻤﯿﻠﻗﻻا ﻰﻤﻈﻌﻟا and they are on the verge of crowning Iran as the most powerful state in the region 3.4.2.3 Negative – Other (25 posts) ﻰﺘﺣ نﻻا 4100 ةرﺎﻏ ﻟ ﻠ ﺎﺤﺘ ﻒﻟ ﻰﻠﻋ د ﺶﻋا و ﻟ ﻢ ﺖﻤﯾ ﺪﺣا Up to now there have been 4,100 air strikes on ةرﺎﻏ ﻰﻠﻋ ا ﻟ قاﺮﻌ ﻦﻣ ا ﻜﯾﺮﻣ ﺎ 100 ﺪﯿﮭﺷ ةرﺎﻏ ﻰﻠﻋ ةﺰﻏ ﻦﻣ ISIS and not a single person has died – 100 اﺮﺳا ﺋ ﻞﯿ 200 ﺮﺟ ﯾ ﺢ ا ﻧ ﻮ !!؟ﻒﯿﻛ ﻲﻜﺤﯾ_نﻮﻨﺠﻣ# martyrs were killed in a single American strike in Iraq, 200 were wounded in a single Israeli strike in Gaza – how is this possible? #crazy_laughing

11 ﺲﻣﻷﺎﺑ تﺰﻏ ﺎﻜﯾﺮﻣأ نﺎﺘﺴﻧﺎﻐﻓأ ﺔﺠﺤﺑ ﺔﺑرﺎﺤﻣ بﺎھرإ .ةﺪﻋﺎﻘﻟا Yesterday, America invaded Afghanistan on ﺎھﺪﻌﺑو تﺰﻏ ا قاﺮﻌﻟ ﺔﺠﺤﺑ ﺔﺤﻠﺳا ا ﻟ رﺎﻣﺪ ا .ﻞﻣﺎﺸﻟ نﻵاو -the pretext of fighting al-Qaeda terrorists. Af ﺳور ﯿ ﺎ ﺗ وﺰﻐ رﻮﺳ ﯾ ﺎ ﺑ ﺔﺠﺤ ﻣ ﻜ ﺎ ﺔﺤﻓ د ا ﺶﻋ ter that, they invaded Iraq on the pretext of weapons of mass destruction. And now Russia is invading Syria on the pretext of combatting ISIS. ﻊﺑرأ تاﻮﻨﺳ او ﺐﻌﺸﻟ ا يرﻮﺴﻟ ﺚﯿﻐﺘﺴﯾ ﻼﺑ ،،ىوﺪﺟ ﺔﻌﻀﺑ For four years the Syrian people have called لﺎﺟر ﻢﮭﺘﻌﻨﺻ أ ناﺮﯾ ﺑ ﻢﺳﺎ د ﺶﻋا ﺎﺤﺗ ﻟ اﻮﻔ ﻞﻛ ا بﺮﻌﻟ ﻊﻣ for aid in vain, then Iran takes a small group of ﺎﻜﯾﺮﻣأ ﻟ ﺎﺘﻘ ﻢﮭﻟ ،، ﻢﻜﻨﯾو ﻦﻣ رﺎﺸﺑ !؟،،ﮫﺘﯿﻧﺎﺑزو men, calls them ISIS, and Arabs everywhere ally with America to fight them – where are you when it comes to Bashar and his ilk?! 3.4.2.4 Neutral (76 posts) راﺮﻗ ﺑوا ﺎﻣﺎ ﯾ ﻦﻤﻀﺘ ﯾز ﺎ ةد ا تارﺎﻐﻟ ا ﺔﯾﻮﺠﻟ ﺪﺿ د# ﺶﻋا ﻦﻣ Obama’s decision guarantees an increase of air # ﯿﻛﺮﺗ ﺎ ﺜﻜﺗو ﻒﯿ ا ﻂﻐﻀﻟ ﻰﻠﻋ نﺪﻣ د# ﺶﻋا ا ﺋﺮﻟ ﺔﯿﺴﯿ د ﻞﺧا -strikes from #Turkey against ISIS and an in رﻮﺳ# ﯾ ﺎ و # ا ﻟ ﻌ ﺮ ا ق - كرﻮﯾﻮﯿﻧ ﺰﻤﯾﺎﺗ crease of pressure on the main cities controlled by #ISIS in #Syria and #Iraq – New York Times ﺖﯿﺒﻟا ﺾﯿﺑﻷا ﺪﻛﺆﯾ ﻞﺘﻘﻣ ﻞﺟﺮﻟا ﻲﻧﺎﺜﻟا ـﺑ ﺶﻋاد# ةرﺎﻐﺑ -The White House confirms the death of a sec قاﺮﻌﻟﺎﺑ نﻵا_ثﺪﺤﯾ# ond #ISIS member in an air strike in Iraq #hap- http://t.co/YGYENrTG81 pening_now :ﻮﯾﺪﯿﻓ تﺎﻄﻘﻟ ﻞﺒﻗ ﻦﯿﻣﺎﻋ ﻲﻔﺤﺼﻠﻟ ﻲﻜﯾﺮﻣﻷا ﺲﻤﯿﺟ“ ”ﻲﻟﻮﻓ Video: Clips of the American journalist ءﺎﻨﺛا ﮫﺘﯿﻄﻐﺗ ثاﺪﺣﻸﻟ ﻲﻓ ﺎﯾرﻮﺳ ﻞﺒﻗ هﺮﺤﻧ ﻰﻠﻋ ﺪﯾ ﺶﻋاد James Foley” from two years ago during his“ coverage of the events in Syria, before his death at the hands of ISIS 3.4.2.5 Irrelevant (13 posts) ﺶﻋاد دﺪﮭﺗ يا ﺺﺨﺷ ﺪھﺎﺸﯾ ﺒﻣ ارﺎ ة ﷼ ﯾرﺪﻣ ﺪ ﮫﻧﻮﻠﺷﺮﺑو ISIS threatens anyone who watches the Real "ـﺑ 80 ﻠﺟ "ةﺪ نﻷ ةﺮﻛ ا ﻟ ﻘ مﺪ ﻦﻣ ا ﻧ ﻨ ﺘ جﺎ ا ﻟ بﺮﻐ ا ﺎﺴﻟ ﻞﻓ Madrid and Barcelona match with 80 lashes because soccer is a product of the despicable West @waiel65 ﻌﻣ ﺎ د ﻟ ﺔ ﯾﺮﺼﻣ ﺔ ةﺮﯿﻄﺧ و ﻛذﻸﻟ ﯿ ﺎ ء ﻓ ﻂﻘ A dangerous Egyptian equilibrium and only ا# ﻲﺴﯿﺴﻟ ناﻮﺧﻻا# ﺶﻋاد# # ﺎﻜﯾﺮﻣا ﺮﺼﻣ# ﺖﯾﻮﺗر the smart #Sisi #Brotherhood #ISIS #America #Egypt Retweet :ﻮﯾﺪﯿﻓ ﺚﺣﺎﺑ ﻲﻛﺮﯿﻣأ ﻖﯾﺪﺻ ﻲﻓﺎﺤﺼﻟا حﻮﺑﺬﻤﻟا ىﺪﺤﺘﯾ Video: An American research who was a ا“ ﻠﺨﻟ ﯿ ﺔﻔ ا ”ﻲﺸﻋاﺪﻟ ﻨﻤﺑ ةﺮظﺎ ﻦﻋ مﻼﺳﻹا friend of the slain journalist challenges the “ISIS caliphate” to a debate about Islam

12 4 Additional Results from Main Analyses 4.1 Temporal Variation in US and ISIS Analyses

In the paper, we note, in very general terms, the temporal patterns in negative traffic related to the US and ISIS. In this section, we use more detailed figures to provide a more in-depth analysis of these patterns. These figures—Figure 1 and Figure 2—show the volume of posts per category for the US and ISIS Analyses from January 1, 2014 to December 31, 2015. The volume of traffic estimated to be irrelevant by the ReadMe-Based Model is excluded from analysis.

As noted in the paper, negative traffic in both analyses increases dramatically in mid-2014, coin- ciding with the beginning of non-covert US involvement against ISIS in Iraq and Syria. In mid- June, numerous Western and non-Western news sources reported requests from the Iraqi govern- ﺎﻜﯾﺮﻣأ ﺪﻌﺘﺴﺗ .ment for limited US air strikes, prompting negative responses on Arabic Twitter (e.g i.e. “America is preparing to invade Iraq for a third time to , وﺰﻐﻟ ا قاﺮﻌﻟ ةﺮﻣ ﺎﺛ ﻟ ﮫﺜ ﻟ هﺮﻣﺪﺘ اﺮﯿﻣﺪﺗ ﺔﺠﺤﺑ ﺶﻋاد ﺔﺠﺤﺑ اﺮﯿﻣﺪﺗ هﺮﻣﺪﺘ ﻟ ﮫﺜ ﻟ ﺎﺛ ةﺮﻣ قاﺮﻌﻟ ا وﺰﻐﻟ complete destroy it for the sake of [destroying] ISIS”). This resulted in a high amount of sustained negative traffic about the US from early June to early August, with a peak in mid-June of approximately 170k negative posts about the US. This negative traffic tapered off until, on August 8, the US began to carry out air strikes against ISIS targets in Iraq, spurring another surge of negative traffic about the US. In fact, August 8 saw the highest amount of negative traffic about the US— approximately 300k posts—for any single day. Similar spikes in traffic occur on September 11, on the anniversary of the 9-11 attacks, and on September 23, when the US begins air strikes in Syria. As US involvement remains ongoing, intermittent spikes in negative traffic occur throughout the time period of our analysis.

In the ISIS Analysis, some of the spikes in negative traffic toward ISIS occur at the same time because of developments in ISIS’s control over the region as well as atrocities ISIS carried out during this time period. For instance, the first spike in negative traffic occurs as ISIS takes Mosul in early June, and another spike occurs in response to ISIS posting images of Shiite shrines being destroyed on August 5. However, the largest spikes in negative traffic about ISIS occur much later in our date range of interest and correspond to ISIS attacks and killings either in the West (e.g. the November 2015 attacks in France) or in the Middle East (e.g. the killing of a downed Jordanian pilot). The largest number of negative posts about ISIS for a single day occurs on February 3, 2015, when a video of downed Jordanian pilot Muath Al-Kasasbeh was distributed by an ISIS- linked account on Twitter and over 600k negative Arabic posts were produced in response.

Two additional patterns are worth noting. First, although peaks in negative traffic toward ISIS are generally larger than those toward the US—600k posts for the highest volume day in negative traffic toward ISIS, vs. 300k posts for the US—there is a larger baseline of negative traffic about the US. Low-volume days for negativity in the US Analysis are still much higher than low-volume days for negativity in the ISIS Analysis. Second, there is a noticeable downward trend in positive traffic in the ISIS Analysis. ISIS has gained notoriety for its astute social media recruitment strat- egies, and much ISIS activity went unchecked on Twitter even up to mid-2014. However, after ISIS’s main Twitter account (@Nnewsi) live-Tweeted its advance on Mosul in June 2014, Twitter

13 shut down the account due to violations of its terms of service.1 Since that point in time, Twitter has periodically cleared out ISIS-associated accounts, including during our date range of interest.2 This periodic deletion of ISIS-related accounts, as well as users’ reactions to increasing attacks in Middle Eastern countries, both likely influence this downward trend.

1 The following link summarizes the suspension of the account in response to graphic images Tweeted at the time: https://www.theatlantic.com/international/archive/2014/06/should-twitter-have-suspended-the-violent-isis-twitter- account/372805/ 2 See https://nyti.ms/2kKeJkX for an example.

14 Figure 1 – US Analysis, volume over time by category

Figure 2 – ISIS Analysis, volume over time by category

4.2 Aggregate Analysis of US-ISIS Sentiment Correlation

To look at correlation between sentiment toward the US and ISIS at the aggregate level, we cal- culate the correlation between the daily volumes for categories in the US Analysis and the ISIS analysis, resulting in the 5x3 correlation matrix found in Table 1. The categories from the US Analysis compose the rows, and the columns are comprised of categories from the ISIS Analysis. Some of the results from this table are unsurprising; for instance, Neutral traffic in the General ISIS analysis is weakly correlated with Neutral Social traffic, and strongly correlated with Neu- tral Political traffic, in the General US analysis. But the results for Negative Political traffic in the General US analysis were surprising: this traffic is strongly correlated with both Negative ISIS traffic (with the highest correlation value in the matrix, at 0.41) and Positive ISIS traffic (at 0.31). This indicates that, at the aggregate level, peaks in negativity towards the US tend to occur simultaneously with peaks in negativity and positivity towards ISIS.

It was a desire to further explore this aggregate correlation that led us to conduct the Group- Based Analysis in the paper. However, before doing so, we checked evidence for a couple of theories that might explain the aggregate correlation. First, we considered the possibility that ISIS- related attacks and killings spur the results, as those condemning the attacks express negativity towards ISIS and those celebrating them express positivity towards ISIS and negativity towards the US. As outlined in Section 7 of this document, we examined the correlation between US and ISIS negative traffic surrounding a set of 77 ISIS-related attacks and killings, and we find little evidence for this explanation of the correlation.

Another explanation for the coincidence of negativity towards the US and ISIS in this aggregate analysis is that US intervention against ISIS spurs both reactions. Such intervention is controversial given past US involvement in the region, thus producing negativity towards the US; in addition, intervention against ISIS may bring the Islamic State and its violent acts more prominently

15 into the news and the ire of the public, thus increasing negativity towards ISIS. US intervention against ISIS was not a one-time occurrence, and for this reason we explore this explanation by examining moving correlation values across the time range of our analyses. In Figure 3, we plot negative political US traffic with negative ISIS traffic, and include shaded vertical lines for each day. The darker the shade of the line, the higher the moving monthly correlation between the two trends. Examining the figure, we find that positive correlation between negative political US traffic and negative ISIS traffic seems to cluster around particular periods of time. For instance, there is high correlation between negative US and negative ISIS traffic during the lead-up to (June 2014) and beginning of (August to October 2014) the U.S. military campaign against ISIS. These results provide preliminary evidence for a correlation between US intervention and negative sentiment towards both the US and ISIS.

Table 1 – Correlation between daily volumes for categories in the General US and ISIS analyses.

Gen. ISIS Cat. Gen. US Cat. Negative Neutral Positive Negative Political 0.41 0.06 0.31 Negative Social 0.09 0.16 -0.13 Neutral Political 0.17 0.37 -0.23 Neutral Social -0.05 -0.04 0.10 Positive -0.10 -0.33 0.17

16 Figure 3 – Temporal trends for negative traffic in the US and ISIS Analyses. The grey vertical lines are darker in areas where there is a higher value for the moving monthly correlation value between these two quantities.

4.3 Additional Results from the Group-Based Analysis

Figure 4 and Figure 5 provide a more in-depth view of the results of the Group-Based Analysis than what was featured in our paper. Figure 4 shows the correlation between negativity toward ISIS and each category of traffic in the US Analysis, with the top panel corresponding to the figure presented in the paper that summarized these results. Of note from this figure is the fact that negative social traffic about the US is, similar to negative political traffic, positively correlated with negativity toward ISIS. All other categories of US-related traffic are negatively correlated with negativity toward ISIS.

Figure 5 shows temporal variation in the Group-Based Analysis, with each colored line corresponding to one of our 16 analysis groups and the gray line present in all four frames corresponding to the overall trend. This figure corresponds to the third panel of Figure 2 in our paper, which shows the same results for all high-ISIS negativity users as a single group. The main patterns of note in this figure are that (1) for almost all of the 16 groups, negativity toward the US peaks during the period of initial intervention, and (2) this peak reaches almost 100% of traffic for those who are more negative toward ISIS. This second point is especially notable in the period of initial US airstrikes against ISIS in Iraq and Syria (August to October 2014). For the eight lower-negativity groups of users, it is only the mid-June leadup to the airstrikes that elicits additional negativity. For the eight higher-negativity groups of users, this August to October period is a sustained period of anti-US sentiment, and this sentiment appears to be higher for those with a larger volume of Twitter posts.

Figure 4 – General US Analysis, by ISIS sub-groups. On the x-axis, users are categorized by the ratio of their negative to non-negative Tweets in the General ISIS sample. The colors represent the number of posts in the ISIS Analysis sample, and results show the percent of traffic in the General US Analysis. The key panel is the one at the top, which shows that the more negative individuals are toward ISIS (on the horizontal axis), the greater the proportion of negative political content in the General US analysis, on average, from those individual.

Figure 5 – This figure shows the trend lines for each of the points in Figure 4. The key point is that the correlation between anti-ISIS and anti-US sentiment is highest, especially for users with high negative sentiment towards ISIS, in early stages of the US offensive against ISIS, June-October 2014. The faint gray line in each panel shows the overall trend for all users during the time period.

19 5 Detailed Content Analysis

We wanted to investigate the ways in which users connect the US and ISIS in their Twitter posts, a task we felt to be best suited to a detailed content analysis. We randomly sampled 25 users from each of the high ISIS-negativity groups, for a total of 100 users, and then manually examined all of their Twitter posts in our US, ISIS, and Combined Analyses.

We tracked this content analysis using the spreadsheet called “Detailed Content Analysis.xlsx”, which is included in our replication folder at the following link: https://www.drop- box.com/s/nocc96dhes8ojvc/Detailed%20Content%20Analysis.xlsx?dl=0. We kept track of the following data points for each individual:

• Did the user have any posts that connected the US and ISIS in a conspiratorial manner? (saved as column “Causal – Consp”) • If the user did not have any conspiratorial posts, did they have posts that connected the US and ISIS in a non-conspiratorial manner? (saved as column “Causal – Non-Consp”) • If the user did not have any posts connecting the US and ISIS, did they mention both entities negatively in an independent manner? (saved as column “Independent”) • Did the user only have posts that were neutral or news-related? (saved as column “News/Neutral”) • Did the user have posts that mentioned both entities in another manner? (saved as column “Other”) • Was it impossible to determine the correct classification for the user? (saved as column “Can't Determine”)

The full results are included in the spreadsheet; as a summary, 43 users had at least one post causally connecting the US and ISIS in a conspiratorial manner. Within the spreadsheet, example posts are included in the “Examples 1” and “Examples 2” columns showing the posts that led to the classification of that user.

20 6 Alternative Hypotheses

We were surprised by the finding that negativity towards the US is correlated with negativity towards ISIS; our own surprise led us to rigorously consider five alternative hypotheses for this correlation. In the paper, we outline the alternative hypotheses and our evidence against them very briefly. Here, we provide more details on the steps we took to address the alternative hypotheses. 6.1 The Ecological Fallacy Hypothesis

As noted in the paper, we considered a Group-Based Analysis superior to other potential ap- proaches to studying the correlation between sentiment towards the US and ISIS. Two other ap- proaches could be considered in this context. The first is an aggregate level approach, where temporal correlation between negativity towards the two entities in our US Analysis and ISIS Analysis is taken to indicate correlation in sentiment—we conducted such an analysis, and our results can be found in Table 1 and Figure 3. However, this approach is flawed due to a lack of ecological validity, since we are really interested in correlation at the individual level. The second potential approach is an individual-level approach, where sentiment towards the US and ISIS is estimated for each individual in the dataset and then a correlation is calculated. This analysis is attractive because it measures attitudes at the appropriate level, but it is also unfeasible for most users because estimating sentiment based on Twitter posts requires a large number of texts per individual, which we do not have in our dataset. For these reasons, we decided that the Group-Based Analysis was the best compromise between these two options, and it is the analysis that we use to establish correlation between anti-US and anti-ISIS sentiment in the paper. However, because the Group-based Analysis is not truly individual-level, it may also suffer from ecological validity. Perhaps our results are driven by something at the group level, such that we are detecting a confluence of negative views at the group-level even though none exists at the individual level. To test this hypothesis, we decided to conduct a truly individual-level analysis among the subset of Twitter users for whom this is possible—those with a large number of posts. To do this, we randomly selected 80 high-volume (10+ posts in our sample) users, 20 from each ISIS negativity level. We then estimated the US Analysis category proportions for each of them individually and average the results within ISIS negativity level. The results appear in Figure 6 and largely confirm the results of the Group-based Analysis in our paper. On average, the US- related traffic for the 20 users in the lowest ISIS negativity level constituted only ~20% of their total US-related traffic, whereas for those in the highest ISIS negativity level the corresponding proportion is ~80%, representing a 60 percentage point increase as you move from the lowest to the highest ISIS negativity level. This is lower than the 83-percentage point increase estimated from the Group-based Analysis, but it is still clearly a significant increase. One important difference to note between this individual-level analysis and the Group- based Analysis are the differing findings for content in the negative social category. The individual-level analysis in this section finds a negative correlation between ISIS negativity and the proportion of a user’s US-related traffic that is categorized as negative social, whereas the Group- based Analysis found the opposite (compare to Figure 4). We do not know the reason for this difference, but because negative political traffic is the main focus of our analysis (as well as the larger of the two negative categories in the US analysis), we do not consider this difference to be a major issue.

Figure 6 – Correlation between negativity toward ISIS and the US at the individual level.

6.2 The News Organization Hypothesis

News organizations and aggregators are a major source of content on Twitter, and we were worried that news organizations in and of themselves might account for the correlation between negativity towards the US and ISIS. Under this explanation, “not being a news organization” is a confounder in the correlation we observe. All of the neutral traffic in both analyses would dominated by news organizations, and the negative traffic just represents anyone expressing an actual opinion. To address alternative hypothesis, we conduct our analysis across different volume levels. We argue that news organizations generally Tweet at a much higher volume than others; therefore, if news organizations were driving our results, we would expect the correlation we observe to not exist (or be much weaker) for high-volume accounts. However, as shown in figures in the paper as well as Figure 4 and Figure 5 in this Appendix, we find that the opposite is true. The smallest correlation we observed was with the lowest volume accounts, and high-volume accounts drive much of the correlation we actually observe.

22 6.3 The “Angry Uncle” Hypothesis

Perhaps a general predisposition towards negativity characterizes our anti-ISIS sub-groups, such that the correlation we observe is not limited to ISIS and the US (imagine the social media tirades of an “angry uncle”).

The most obvious empirical implication of this theory is that we should see a correlation between negativity towards ISIS and the negativity of non-US and non-ISIS traffic. The strongest empirical evidence in favor of this claim would be a correlation as strong as the one between negative ISIS and US traffic; the strongest evidence against this claim would be no correlation, in spite of that between negative ISIS and US traffic. Testing these correlations requires conducting an analysis of non-US and non-ISIS traffic from our 16 groups of users and then comparing the correlation between ISIS negativity and negativity in this new analysis to the correlation between ISIS negativity and negativity in the US analysis.

To conduct this analysis, we queried non-US and non-ISIS traffic for our users by using the top 30–40 words from a popular Arabic frequency dictionary (Buckwalter and Parkinson 2011) and excluding results that contain our US and ISIS keywords. This produces an analysis on, effectively, the universe of users’ non-US and non-ISIS posts. This traffic is so varied that training an analysis ourselves was not feasible; for this reason, we used the Sentiment Classifier (the third model dis- cussed in Section 2 of this Appendix) to determine the percentage of the traffic that is negative. We also ran this third type of model on US traffic, and we use the US sentiment analysis in this section to allow for a fair comparison.

The results of these sentiment analyses can be found in Figure 7. The results of these sentiment analyses indicate that general negativity may account for some, but not all, of the relationship we observe between negativity towards ISIS and the US. Across each of the four volume groups, going from the lowest to the highest ISIS negativity level was associated with in increase on average of 10 percentage points in the negative proportion of non-ISIS/non-US traffic. However, this increase in negative traffic is overshadowed by the corresponding increase in the negative proportion of US-related traffic, where going from the lowest to the highest ISIS negativity level is associated with an average increase of 17 percentage points. It should be noted that the average increase in negativity we observed in our user-trained analysis of US traffic was much larger, and therefore this test may be underestimating the proportion of correlation that can be explained by this alternative hypothesis. However, that aside, the results of the test itself provide evidence that the “angry uncle” hypothesis does not fully explain our findings.

Figure 7 – Sentiment analysis showing correlation between ISIS negativity and US/non-US traffic

6.3.1 Keywords for “Angry Uncle” Analysis Below are the keywords and Boolean operators that we used to select Arabic traffic that did not include our US or ISIS keywords.

OR " ﻰﻠﻋ " OR " ﻦﻤﻓ " OR " ﻦﻤﺑ " OR " ﻦﻤﻟ " OR " ﻦﻣو " OR " ﻦﻣ " OR " ﻰﻔﻓ " OR " ﻲﻔﻓ " OR " ﻲﻓو " OR " ﻲﻓ ") OR " ﻠﻋ ﺎﮭﯿ " OR " ﻠﻋ ﮫﯿ " OR " ﻠﻋ ﻲﻜﯿ " OR " ﻠﻋ ﻚﯿ " OR " ﻠﻋ ﻲﯿ " OR " ﻠﻋ ﯿ ﺎ " OR " ﻲﻠﻋ " OR " ﻰﻠﻌﻓ " OR " ﻋو ﻠ ﻰ " " ﻋو ﻠ ﻲﯿ " OR " ﻋو ﻠ ﯿ ﺎ " OR " ﻋو ﻠ ﻲ " OR " ﻠﻋ ﻦﮭﯿ " OR " ﻠﻋ ﻢﮭﯿ " OR " ﻠﻋ ﻦﻜﯿ " OR " ﻠﻋ ﻮﻜﯿ " OR " ﻠﻋ ﻢﻜﯿ " OR " ﻠﻋ ﯿ ﻨ ﺎ " " ﻋو ﻠ ﯿ ﻦﻜ " OR " ﻋو ﻠ ﯿ ﻜ ﻮ " OR " ﻋو ﻠ ﯿ ﻜ ﻢ " OR " ﻋو ﻠ ﯿ ﻨ ﺎ " OR " ﻋو ﻠ ﯿ ﮭ ﺎ " OR " ﻋو ﻠ ﯿ ﮫ " OR " ﻋو ﻠ ﯿ ﻲﻜ " OR " ﻋو ﻠ ﻚﯿ " OR OR " ﻠﻌﻓ ﮫﯿ " OR " ﻠﻌﻓ ﻲﻜﯿ " OR " ﻠﻌﻓ ﻚﯿ " OR " ﻠﻌﻓ ﻲﯿ " OR " ﻠﻌﻓ ﯿ ﺎ " OR " ﻲﻠﻌﻓ " OR " ﻋو ﻠ ﯿ ﻦﮭ " OR " ﻋو ﻠ ﯿ ﮭ ﻢ " OR OR " نا " OR " نأ " OR " ﻠﻌﻓ ﮭﯿ ﻦ" OR " ﻠﻌﻓ ﻢﮭﯿ " OR " ﻠﻌﻓ ﻦﻜﯿ " OR " ﻠﻌﻓ ﻮﻜﯿ " OR " ﻠﻌﻓ ﻢﻜﯿ " OR " ﻠﻌﻓ ﯿ ﻨ ﺎ " OR " ﻠﻌﻓ ﺎﮭﯿ " " ﻲﻧأ " OR " نﺈﻓ " OR " نﺎﻓ " OR " نﺄﻓ " OR " نﺈﺑ " OR " نﺎﺑ " OR " نﺄﺑ " OR " نإو " OR " ناو " OR " نأو " OR " نإ " OR " او ﻧ ﻲﻨ " OR " او ﻲﻧ " OR " أو ﻧ ﻲﻨ " OR " أو ﻲﻧ " OR " ﻲﻨﻧإ " OR " ﻲﻧإ " OR " ﻲﻨﻧا " OR " ﻲﻧا " OR " ﻲﻨﻧأ " OR " ﻚﻧا " OR " ﻚﻧأ " OR " ﻲﻨﻧﺈﻓ " OR " ﻲﻧﺈﻓ " OR " ﻲﻨﻧﺎﻓ " OR " ﻲﻧﺎﻓ " OR " ﻲﻨﻧﺄﻓ " OR " ﻲﻧﺄﻓ " OR " إو ﻧ ﻲﻨ " OR " إو ﻲﻧ " " ﮫﻧإ " OR " ﮫﻧا " OR " ﮫﻧأ " OR " ﻚﻧﺈﻓ " OR " ﻚﻧﺎﻓ " OR " ﻚﻧﺄﻓ " OR " إو ﻚﻧ " OR " او ﻚﻧ " OR " أو ﻚﻧ " OR " ﻚﻧإ " OR " أو ﺎﮭﻧ " OR " ﺎﮭﻧإ " OR " ﺎﮭﻧا " OR " ﺎﮭﻧأ " OR " ﮫﻧﺈﻓ " OR " ﮫﻧﺎﻓ " OR " ﮫﻧﺄﻓ " OR " إو ﮫﻧ " OR " او ﮫﻧ " OR " أو ﮫﻧ " OR " او ﻧ ﻨ ﺎ " OR " أو ﻧ ﻨ ﺎ " OR " ﺎﻨﻧإ " OR " ﺎﻨﻧا " OR " ﺎﻨﻧأ " OR " ﺎﮭﻧﺈﻓ " OR " ﺎﮭﻧﺎﻓ " OR " ﺎﮭﻧﺄﻓ " OR " إو ﺎﮭﻧ " OR " او ﺎﮭﻧ " OR OR " او ﻢﻜﻧ " OR " أو ﻢﻜﻧ " OR " ﻢﻜﻧإ " OR " ﻢﻜﻧا " OR " ﻢﻜﻧأ " OR " ﺎﻨﻧﺈﻓ " OR " ﺎﻨﻧﺎﻓ " OR " ﺎﻨﻧﺄﻓ " OR " إو ﻧ ﻨ ﺎ " OR OR " او ﻦﻜﻧ " OR " أو ﻦﻜﻧ " OR " ﻦﻜﻧإ " OR " ﻦﻜﻧا " OR " ﻦﻜﻧأ " OR " ﻢﻜﻧﺈﻓ " OR " ﻢﻜﻧﺎﻓ " OR " ﻢﻜﻧﺄﻓ " OR " إو ﻢﻜﻧ " OR " او ﻢﮭﻧ " OR " أو ﻢﮭﻧ " OR " ﻢھإ " OR " ﻢﮭﻧا " OR " ﻢﮭﻧأ " OR " ﻦﻜﻧﺈﻓ " OR " ﻦﻜﻧﺎﻓ " OR " ﻦﻜﻧﺄﻓ " OR " إو ﻦﻜﻧ " OR " او ﻦﮭﻧ " OR " أو ﻦﮭﻧ " OR " ﻦﮭﻧإ " OR " ﻦﮭﻧا " OR " ﻦﮭﻧأ " OR " ﻢﮭﻧﺈﻓ " OR " ﻢﮭﻧﺎﻓ " OR " ﻢﮭﻧﺄﻓ " OR " إو ﻢﮭﻧ " " ﻰﻟﺎﻓ " OR " ﻰﻟﺈﻓ " OR " او ﻰﻟ " OR " إو ﻰﻟ " OR " ﻰﻟا " OR " ﻰﻟإ " OR " ﻦﮭﻧﺈﻓ " OR " ﻦﮭﻧﺎﻓ " OR " ﻦﮭﻧﺄﻓ " OR " إو ﻦﮭﻧ " OR " إو ﻟ ﻚﯿ " OR " ﻚﯿﻟا " OR " ﻚﯿﻟإ " OR " ﻲﻟﺎﻓ " OR " ﻲﻟﺈﻓ " OR " او ﻲﻟ " OR " إو ﻲﻟ " OR " ﻲﻟا " OR " ﻲﻟإ " OR " ﺎﮭﯿﻟإ " OR " ﮫﯿﻟﺎﻓ " OR " ﮫﯿﻟﺈﻓ " OR " او ﻟ ﮫﯿ " OR " إو ﻟ ﮫﯿ " OR " ﮫﯿﻟا " OR " ﮫﯿﻟإ " OR " ﻚﯿﻟﺎﻓ " OR " ﻚﯿﻟﺈﻓ " OR " او ﻟ ﻚﯿ "

24 OR " او ﻟ ﯿ ﻨ ﺎ " OR " إو ﻟ ﯿ ﻨ ﺎ " OR " ﺎﻨﯿﻟا " OR " ﺎﻨﯿﻟإ " OR " ﺎﮭﯿﻟﺎﻓ " OR " ﺎﮭﯿﻟﺈﻓ " OR " او ﻟ ﺎﮭﯿ " OR " إو ﻟ ﺎﮭﯿ " OR " ﺎﮭﯿﻟا " OR OR " ﻦﻜﯿﻟإ " OR " ﻢﻜﯿﻟﺎﻓ " OR " ﻢﻜﯿﻟﺈﻓ " OR " او ﻟ ﻢﻜﯿ " OR " إو ﻟ ﻢﻜﯿ " OR " ﻢﻜﯿﻟا " OR " ﻢﻜﯿﻟإ " OR " ﺎﻨﯿﻟﺎﻓ " OR " ﺎﻨﯿﻟﺈﻓ " " او ﻟ ﻢﮭﯿ " OR " إو ﻟ ﻢﮭﯿ " OR " ﻢﮭﯿﻟا " OR " ﻢﮭﯿﻟإ " OR " ﻦﻜﯿﻟﺎﻓ " OR " ﻦﻜﯿﻟﺈﻓ " OR " او ﻟ ﻦﻜﯿ " OR " إو ﻦﻜﯿﻟ " OR " ﻦﻜﯿﻟا " " ﻦﮭﯿﻟإ " OR " ﻢﮭﯿﻟﺎﻓ " OR " ﻢﮭﯿﻟﺈﻓ " OR " ﻢﮭﯿﻟا " OR " ﻢﮭﯿﻟإ " OR " او ﻟ ﻢﮭﯿ " OR " إو ﻟ ﻢﮭﯿ " OR " ﻢﮭﯿﻟﺎﻓ " OR " ﻢﮭﯿﻟﺈﻓ " OR " ﺎﻛ ﺖﻧ " OR " نﺎﻛ " OR " ﻨﻛ ﻲﺘ " OR " ﺖﻨﻛ " OR " ﻦﮭﯿﻟﺎﻓ " OR " ﻦﮭﯿﻟﺈﻓ " OR " او ﻟ ﻦﮭﯿ " OR " إو ﻟ ﻦﮭﯿ " OR " ﻦﮭﯿﻟا " OR OR " نﻮﻜﺗ " OR " ﻦﻛا " OR " ﻦﻛأ " OR " نﻮﻛا " OR " نﻮﻛأ " OR " ﺎﻛ اﻮﻧ " OR " ﻨﻛ اﻮﺘ " OR " ﻨﻛ ﻢﺘ " OR " ﻨﻛ ﺎ " OR OR " نﻮﻧﻮﻜﯾ " OR " اﻮﻧﻮﻜﺗ " OR " نﻮﻧﻮﻜﺗ " OR " ﻦﻜﻧ " OR " نﻮﻜﻧ " OR " ﻦﻜﯾ " OR " نﻮﻜﯾ " OR " ﻲﻧﻮﻜﺗ " OR " ﻦﻜﺗ " OR " ﺘﺳ نﻮﻧﻮﻜ " OR " ﻨﺳ نﻮﻜ " OR " ﯿﺳ نﻮﻜ " OR " ﺘﺳ ﻲﻧﻮﻜ " OR " ﺘﺳ نﻮﻜ " OR " ﺎﺳ نﻮﻛ " OR " ﺄﺳ نﻮﻛ " OR " اﻮﻧﻮﻜﯾ " OR " ﻛو ﻨ ﺎ " OR " ﻛو ﺎ ﺖﻧ " OR " ﻛو نﺎ " OR " ﻛو ﻨ ﻲﺘ " OR " ﻛو ﺖﻨ " OR " ﯿﺳ ﻮﻧﻮﻜ ا " OR " ﯿﺳ نﻮﻧﻮﻜ " OR " ﺘﺳ ﻮﻧﻮﻜ ا " " ﻦﻜﺗو " OR " نﻮﻜﺗو " OR " ﻦﻛاو " OR " ﻦﻛأو " OR " نﻮﻛاو " OR " نﻮﻛأو " OR " ﻛو ﺎ ﻧ ﻮ ا " OR " ﻛو ﻨ ﺘ ﻮ ا " OR " ﻛو ﻨ ﺘ ﻢ " " نﻮﻧﻮﻜﯾو " OR " اﻮﻧﻮﻜﺗو " OR " نﻮﻧﻮﻜﺗو " OR " ﻦﻜﻧو " OR " نﻮﻜﻧو " OR " ﻦﻜﯾو " OR " نﻮﻜﯾو " OR " ﻲﻧﻮﻜﺗو " OR OR " ﺳو ﻨ ﻜ نﻮ " OR " ﺳو ﯿ ﻜ نﻮ " OR " ﺳو ﺘ ﻜ ﻮ ﻧ ﻲ " OR " ﺳو ﺘ ﻜ نﻮ " OR " ﺳو ﺎ ﻛ نﻮ " OR " ﺳو ﺄ ﻛ نﻮ " OR " اﻮﻧﻮﻜﯾو " OR "ﷲ" OR " ﻼﺑ " OR " ﻼﻓ " OR " ﻻو " OR "ﻻ" OR " ﺳو ﯿ ﻜ ﻮ ﻧ ﻮ ا " OR " ﺳو ﯿ ﻜ ﻮ ﻧ نﻮ " OR " ﺳو ﺘ ﻜ ﻮ ﻧ ﻮ ا " OR " ﺳو ﺘ ﻜ ﻮ ﻧ نﻮ " OR " ﻚﻨﻋ " OR " ﻲﻨﻋ " OR " ﻦﻌﻓ " OR " ﻦﻋو " OR " ﻦﻋ " OR " ¯ﺎﻤﺴﺑ " OR " ﷲو " OR " ¯ﻻ " OR " ¯ﺎﺑ " OR " ﻨﻋ ﻦﮭ " OR " ﻨﻋ ﻢﮭ " OR " ﻨﻋ ﻮﻜ " OR " ﻨﻋ ﻮﻜ ا " OR " ﻨﻋ ﻢﻜ " OR " ﻨﻋ ﻨ ﺎ " OR " ﻨﻋ ﺎ " OR " ﻨﻋ ﮭ ﺎ " OR " ﻨﻋ ﮫ " OR " ﻨﻋ ﻲﻜ " " ﻦﻠﻗ " OR " اﻮﻟﺎﻗ " OR " ﻦﺘﻠﻗ " OR " ﻢﺘﻠﻗ " OR " ﺎﻨﻠﻗ " OR " ﺖﻟﺎﻗ " OR " ﻞﻗ " OR " لﺎﻗ " OR " ﻲﺘﻠﻗ " OR " ﺖﻠﻗ " OR " لﻮﻘﻧ " OR " ﻞﻘﯾ " OR " لﻮﻘﯾ " OR " ﻲﻟﻮﻘﺗ " OR " ﻞﻘﺗ " OR " لﻮﻘﺗ " OR " ﻞﻗا " OR " ﻞﻗأ " OR " لﻮﻗا " OR " لﻮﻗأ " OR " ﺎﺳ لﻮﻗ " OR " ﺄﺳ لﻮﻗ " OR " ﻦﻠﻘﯾ " OR " اﻮﻟﻮﻘﯾ " OR " نﻮﻟﻮﻘﯾ " OR " ﻦﻠﻘﺗ " OR " نﻮﻟﻮﻘﺗ " OR " اﻮﻟﻮﻘﺗ " OR " ﻞﻘﻧ " OR " ﯿﺳ ﻮﻘ ﻟ نﻮ " OR " ﺘﺳ ﻘ ﻦﻠ " OR " ﺘﺳ ﻮﻘ ﻟ نﻮ " OR " ﺘﺳ ﻮﻘ ﻟ ﻮ ا " OR " ﻨﺳ لﻮﻘ " OR " ﯿﺳ لﻮﻘ " OR " ﺘﺳ ﻮﻘ ﻲﻟ " OR " ﺘﺳ لﻮﻘ " OR " ﻗو ﻠ ﻢﺘ " OR " ﻗو ﻠ ﻨ ﺎ " OR " ﻗو ﺎ ﺖﻟ " OR " ﻞﻗو " OR " ﻗو لﺎ " OR " ﻗو ﻠ ﻲﺘ " OR " ﻗو ﺖﻠ " OR " ﯿﺳ ﻘ ﻦﻠ " OR " ﯿﺳ ﻮﻘ ﻟ ﻮ ا " OR " ﺗو ﻞﻘ " OR " ﺗو لﻮﻘ " OR " او ﻞﻗ " OR " أو ﻞﻗ " OR " او لﻮﻗ " OR " أو لﻮﻗ " OR " ﻗو ﻦﻠ " OR " ﻗو ﺎ اﻮﻟ " OR " ﻗو ﻠ ﻦﺘ " OR OR " ﺗو ﻘ ﻦﻠ " OR " ﺗو نﻮﻟﻮﻘ " OR " ﺗو اﻮﻟﻮﻘ " OR " ﻧو ﻞﻘ " OR " ﻧو لﻮﻘ " OR " ﯾو ﻞﻘ " OR " ﯾو لﻮﻘ " OR " ﺗو ﻲﻟﻮﻘ " OR " ﺳو ﯿ ﻘ لﻮ " OR " ﺳو ﺘ ﻘ ﻮ ﻟ ﻲ " OR " ﺳو ﺘ ﻘ لﻮ " OR " ﺳو ﺎ ﻗ لﻮ " OR " ﺳو ﺄ ﻗ لﻮ " OR " ﯾو ﻘ ﻦﻠ " OR " ﯾو اﻮﻟﻮﻘ " OR " ﯾو نﻮﻟﻮﻘ " OR " ﺳو ﯿ ﻘ ﻠ ﻦ " OR " ﺳو ﯿ ﻘ ﻮ ﻟ ﻮ ا " OR " ﺳو ﯿ ﻘ ﻮ ﻟ نﻮ " OR " ﺳو ﺘ ﻘ ﻠ ﻦ " OR " ﺳو ﺘ ﻘ ﻮ ﻟ نﻮ " OR " ﺳو ﺘ ﻘ ﻮ ﻟ ﻮ ا " OR " ﺳو ﻨ ﻘ لﻮ " OR OR " ﻌﻣ ﺎ ﯾ ﺎ " OR " ﻲﻌﻣ " OR " ﻌﻣ ﺎ " OR " ﻊﻣ " OR " اد " OR " هد " OR " ىﺪھ " OR " ﺪھ ا " OR " ذﺎھ ا " OR " ﺬھ ا " OR " ﻮﻜﻌﻣ ا " OR " ﻮﻜﻌﻣ " OR " ﻦﻜﻌﻣ " OR " ﻢﻜﻌﻣ " OR " ﻌﻣ ﻨ ﺎ " OR " ﮭﻌﻣ ﺎ " OR " ﮫﻌﻣ " OR " ﻌﻣ كﺎ " OR " ﻌﻣ يﺎ " OR " ﻞﻛ " OR " ﻟﻻ ﻲﺘ " OR " ﻲﺘﻟﺎﻓ " OR " او ﻟ ﻲﺘ " OR " ﻲﺘﻟا " OR " ﻦﮭﻌﻣ " OR " ﻤﮭﻌﻣ ﺎ " OR " ﻦﮭﻌﻣ " OR " ﻢﮭﻌﻣ " " ﺎﻧا " OR " ﺎﻧأ " OR " ﻲﻟإ " OR " ﻲﻠﻟإ " OR " ﻲﻟا " OR " ﻲﻠﻟا " OR " يﺬﻟا " OR " وا " OR " وأ " OR " ﺬھ ه " OR " ﻮھ " " ﺎﯾ " OR " ﺪﻌﺑ " OR " ﻲھ " OR " ﻦﯿﺑ " OR " ﺎﻣأ " OR " ﺎﻣ " OR " ﻢﻟأ " OR " ﻢﻟ " OR " مﺎﯾا " OR " مﺎﯾأ " OR " مﻮﯾ " OR " ﻲﺷإ " OR " ﻲﺷا " OR " ﻲﺷ " OR " ﺊﺷ " OR " ءﻲﺷ " OR " ﺮﺧأ " OR " ﺮﺧا " OR " ﺮﺧآ " OR " ﺪﻗ " OR " ﻚﻟذ " OR OR " ﻰﻟوا " OR " ﻰﻟوأ " OR " لوا " OR " لوأ " OR " ﻨﻋ ﺪ " OR " ﯿﺷا ﺎ " OR " ﯿﺷا ءﺎ " OR " ﯿﺷأ ﺎ " OR " ﯿﺷأ ءﺎ " OR " بﺮﻋ " OR " ﺮﻋ ﺑ ﻲ " OR " ﺲﻔﻧا " OR " ﺲﻔﻧأ " OR " ﺲﻔﻧ " OR " اذا " OR " اذإ " OR " ﺮﯿﻏ " OR " ﻻوأ " OR " اوأ ﻞﺋ " ﺎﻜﯾﺮﻣأ - AND (" فﺮﻋ " OR " لﺎﻤﻋأ " OR " ﻞﻤﻋ " OR " ﺳؤر ﺎ ء " OR " ﺋر ﺲﯿ " OR " ﺔﯾأ " OR " يا " OR " يأ " OR - AND ﻣﻻ ﯿ ﻛﺮ ﺎ - AND ﻣﻷ ﯿ ﻛﺮ ﺎ - AND ﺮﻣﻻ ﯾ ﻜ ﺎ - AND ﺮﻣﻷ ﯾ ﻜ ﺎ - AND ﺎﻛﺮﯿﻣا - AND ﺎﻛﺮﯿﻣأ - AND ﺎﻜﯾﺮﻣا - AND ﺎﻛﺮﯿﻣﺎﻓ - AND ﺎﻛﺮﯿﻣﺄﻓ - AND ﺎﻜﯾﺮﻣﺎﻓ - AND ﺎﻜﯾﺮﻣﺄﻓ - AND ﺑ ﺎﻛﺮﯿﻣﺎ - AND ﺎﻛﺮﯿﻣﺄﺑ - AND ﺎﻜﯾﺮﻣﺎﺑ - AND ﺑ ﺎﻜﯾﺮﻣﺄ ﻲﻜﯾﺮﻣﻷا - AND ﻲﻜﯾﺮﻣا - AND ﻲﻜﯾﺮﻣأ - AND ﺎﻛﺮﯿﻣاو - AND ﺎﻛﺮﯿﻣأو - AND ﺎﻜﯾﺮﻣاو - AND ﺎﻜﯾﺮﻣأو - AND ﺮﻣﻻ ﯾ ﻲﻜ - AND ﺮﻣﻷ ﯾ ﻲﻜ - AND ﻲﻛﺮﯿﻣﻻا - AND ﻲﻛﺮﯿﻣﻷا - AND ﻲﻛﺮﯿﻣا - AND ﻲﻛﺮﯿﻣأ - AND ﻲﻜﯾﺮﻣﻻا - AND - AND ﻲﻛﺮﯿﻣﻼﻟ - AND ﻲﻛﺮﯿﻣﻸﻟ - AND ﻣﻻ ﯿ ﻲﻛﺮ - AND ﻣﻷ ﯿ ﻲﻛﺮ - AND ﯾﺮﻣﻼﻟ ﻲﻜ - AND ﯾﺮﻣﻸﻟ ﻲﻜ - AND ﻲﻛﺮﯿﻣﻷﺎﺑ - AND ﻲﻛﺮﯿﻣﺎﺑ - AND ﻲﻛﺮﯿﻣﺄﺑ - AND ﻲﻜﯾﺮﻣﻻﺎﺑ - AND ﻲﻜﯾﺮﻣﻷﺎﺑ - AND ﻲﻜﯾﺮﻣﺎﺑ - AND ﺑ ﻲﻜﯾﺮﻣﺄ - AND ﻛﺮﯿﻣﺄﻓ ﻲ- AND ﻲﻜﯾﺮﻣﻻﺎﻓ - AND ﻲﻜﯾﺮﻣﻷﺎﻓ - AND ﻲﻜﯾﺮﻣﺎﻓ - AND ﻲﻜﯾﺮﻣﺄﻓ - AND ﻲﻛﺮﯿﻣﻻﺎﺑ - AND ﻲﻜﯾﺮﻣﻻاو - AND ﻲﻜﯾﺮﻣﻷاو - AND ﻲﻜﯾﺮﻣاو - AND ﻲﻜﯾﺮﻣأو - AND ﻲﻛﺮﯿﻣﻻﺎﻓ - AND ﻲﻛﺮﯿﻣﻷﺎﻓ - AND ﻓ ﻲﻛﺮﯿﻣﺎ - AND ﺔﯿﻜﯾﺮﻣا - AND ﺔﯿﻜﯾﺮﻣأ - AND ﻲﻛﺮﯿﻣﻻاو - AND ﻲﻛﺮﯿﻣﻷاو - AND ﻲﻛﺮﯿﻣاو - AND ﻲﻛﺮﯿﻣأو - AND AND ﺮﻣﻷ ﯾ ﻜ ﯿ ﺔ - AND ﯿﻛﺮﯿﻣﻻا ﺔ - AND ﯿﻛﺮﯿﻣﻷا ﺔ - AND ﺔﯿﻛﺮﯿﻣا - AND ﺔﯿﻛﺮﯿﻣأ - AND ﯿﻜﯾﺮﻣﻻا ﺔ - AND ا ﺮﻣﻷ ﯾ ﻜ ﯿ ﺔ ﻣ ﻛﺮﯿﻣﻼﻟ ﯿ ﺔ - AND ﻛﺮﯿﻣﻸﻟ ﯿ ﺔ - AND ﻣﻻ ﯿ ﻛﺮ ﯿ ﺔ - AND ﻣﻷ ﯿ ﻛﺮ ﯿ ﺔ - AND ﯾﺮﻣﻼﻟ ﻜ ﯿ ﺔ - AND ﯾﺮﻣﻸﻟ ﻜ ﯿ ﺔ - AND ﺮﻣﻻ ﯾ ﻜ ﯿ ﺔ - - AND ﺔﯿﻛﺮﯿﻣﺎﺑ - AND ﺔﯿﻛﺮﯿﻣﺄﺑ - AND ﺔﯿﻜﯾﺮﻣﻻﺎﺑ - AND ﺔﯿﻜﯾﺮﻣﻷﺎﺑ - AND ﺔﯿﻜﯾﺮﻣﺎﺑ - AND ﺔﯿﻜﯾﺮﻣﺄﺑ - AND ﺔﯿﻛﺮﯿﻣﺄﻓ - AND ﺔﯿﻜﯾﺮﻣﻻﺎﻓ - AND ﺔﯿﻜﯾﺮﻣﻷﺎﻓ - AND ﺔﯿﻜﯾﺮﻣﺎﻓ - AND ﺔﯿﻜﯾﺮﻣﺄﻓ - AND ﺔﯿﻛﺮﯿﻣﻻﺎﺑ - AND ﺑ ﯿﻛﺮﯿﻣﻷﺎ ﺔ ﻛﯿﻷ

25 - AND ﺔﯿﻜﯾﺮﻣﻷاو - AND ﺔﯿﻜﯾﺮﻣاو - AND ﺔﯿﻜﯾﺮﻣأو - AND ﺔﯿﻛﺮﯿﻣﻻﺎﻓ - AND ﺔﯿﻛﺮﯿﻣﻷﺎﻓ - AND ﺔﯿﻛﺮﯿﻣﺎﻓ - AND ﻦﯿﯿﻜﯾﺮﻣا - AND ﻦﯿﯿﻜﯾﺮﻣأ - AND ﺔﯿﻛﺮﯿﻣﻻاو - AND ﺔﯿﻛﺮﯿﻣﻷاو - AND ﺔﯿﻛﺮﯿﻣاو - AND ﺔﯿﻛﺮﯿﻣأو - AND و ﯿﻜﯾﺮﻣﻻا ﺔ ﻜﺮﻻ AND ﯿﻛﺮﯿﻣﻻا ﻦﯿ - AND ﯿﻛﺮﯿﻣﻷا ﻦﯿ - AND ﻦﯿﯿﻛﺮﯿﻣا - AND ﻦﯿﯿﻛﺮﯿﻣأ - AND ﯿﻜﯾﺮﻣﻻا ﻦﯿ - AND ﯿﻜﯾﺮﻣﻷا ﻦﯿ - AND - AND ﻣﻻ ﯿ ﻛﺮ ﯿ ﯿ ﻦ - AND ﻣﻷ ﯿ ﻛﺮ ﯿ ﯿ ﻦ - AND ﯾﺮﻣﻼﻟ ﻜ ﯿ ﻦﯿ - AND ﯾﺮﻣﻸﻟ ﻜ ﯿ ﻦﯿ - AND ﺮﻣﻻ ﯾ ﻜ ﯿ ﯿ ﻦ - AND ﺮﻣﻷ ﯾ ﻜ ﯿ ﯿ ﻦ - - AND ﻦﯿﯿﻜﯾﺮﻣﻻﺎﺑ - AND ﻦﯿﯿﻜﯾﺮﻣﻷﺎﺑ - AND ﻦﯿﯿﻜﯾﺮﻣﺎﺑ - AND ﻦﯿﯿﻜﯾﺮﻣﺄﺑ - AND ﻛﺮﯿﻣﻼﻟ ﯿ ﻦﯿ - AND ﻟ ﻣﻸ ﯿ ﻛﺮ ﯿ ﯿ ﻦ ﺮﯿﻣ - AND ﻦﯿﯿﻜﯾﺮﻣﺎﻓ - AND ﻦﯿﯿﻜﯾﺮﻣﺄﻓ - AND ﻦﯿﯿﻛﺮﯿﻣﻻﺎﺑ - AND ﻦﯿﯿﻛﺮﯿﻣﻷﺎﺑ - AND ﻦﯿﯿﻛﺮﯿﻣﺎﺑ - AND ﺑ ﯿﻛﺮﯿﻣﺄ ﻦﯿﯿﺮﻣ - AND ﻦﯿﯿﻛﺮﯿﻣﻻﺎﻓ - AND ﻦﯿﯿﻛﺮﯿﻣﻷﺎﻓ - AND ﻦﯿﯿﻛﺮﯿﻣﺎﻓ - AND ﻦﯿﯿﻛﺮﯿﻣﺄﻓ - AND ﻦﯿﯿﻜﯾﺮﻣﻻﺎﻓ - AND ﻓ ﯾﺮﻣﻷﺎ ﻜ ﯿ ﻦﯿ ﺮﻷ - AND ﯿﻛﺮﯿﻣاو ﻦﯿ - AND ﯿﻛﺮﯿﻣأو ﻦﯿ - AND ﯿﻜﯾﺮﻣﻻاو ﻦﯿ - AND ﯿﻜﯾﺮﻣﻷاو ﻦﯿ - AND ﯿﻜﯾﺮﻣاو ﻦﯿ - AND و ﻦﯿﯿﻜﯾﺮﻣأ - AND نﻮﯿﻜﯾﺮﻣﻻا - AND نﻮﯿﻜﯾﺮﻣﻷا - AND نﻮﯿﻜﯾﺮﻣا - AND نﻮﯿﻜﯾﺮﻣأ - AND ﯿﻛﺮﯿﻣﻻاو ﻦﯿ - AND و ﯿﻛﺮﯿﻣﻷا ﻦﯿ ﻛﯿﻷ - AND ﺮﻣﻻ ﯾ ﻜ ﯿ نﻮ - AND ﺮﻣﻷ ﯾ ﻜ ﯿ ﻮ ن- AND نﻮﯿﻛﺮﯿﻣﻻا - AND نﻮﯿﻛﺮﯿﻣﻷا - AND نﻮﯿﻛﺮﯿﻣا - AND أ نﻮﯿﻛﺮﯿﻣ - AND ﻛﺮﯿﻣﻼﻟ ﯿ نﻮ - AND ﻛﺮﯿﻣﻸﻟ ﯿ نﻮ - AND ﻣﻻ ﯿ ﻛﺮ ﯿ نﻮ - AND ﻣﻷ ﯿ ﻛﺮ ﯿ نﻮ - AND ﯾﺮﻣﻼﻟ ﻜ ﯿ نﻮ - AND ﻟ ﺮﻣﻸ ﯾ ﻜ ﯿ نﻮ ﻣ - AND نﻮﯿﻛﺮﯿﻣﺎﺑ - AND نﻮﯿﻛﺮﯿﻣﺄﺑ - AND نﻮﯿﻜﯾﺮﻣﻻﺎﺑ - AND نﻮﯿﻜﯾﺮﻣﻷﺎﺑ - AND نﻮﯿﻜﯾﺮﻣﺎﺑ - AND ﺑ نﻮﯿﻜﯾﺮﻣﺄ AND نﻮﯿﻜﯾﺮﻣﻻﺎﻓ - AND نﻮﯿﻜﯾﺮﻣﻷﺎﻓ - AND نﻮﯿﻜﯾﺮﻣﺎﻓ - AND نﻮﯿﻜﯾﺮﻣﺄﻓ - AND نﻮﯿﻛﺮﯿﻣﻻﺎﺑ - AND ﺑ نﻮﯿﻛﺮﯿﻣﻷﺎ - AND نﻮﯿﻜﯾﺮﻣاو - AND نﻮﯿﻜﯾﺮﻣأو - AND نﻮﯿﻛﺮﯿﻣﻻﺎﻓ - AND نﻮﯿﻛﺮﯿﻣﻷﺎﻓ - AND نﻮﯿﻛﺮﯿﻣﺎﻓ - AND نﻮﯿﻛﺮﯿﻣﺄﻓ - نﻮﯿﻛﺮﯿﻣﻻاو - AND نﻮﯿﻛﺮﯿﻣﻷاو - AND نﻮﯿﻛﺮﯿﻣاو - AND نﻮﯿﻛﺮﯿﻣأو - AND نﻮﯿﻜﯾﺮﻣﻻاو - AND و نﻮﯿﻜﯾﺮﻣﻷا نﺎﻛﺮﯿﻣﻷا - AND نﺎﻛﺮﯿﻣا - AND نﺎﻛﺮﯿﻣأ - AND نﺎﻜﯾﺮﻣﻻا - AND نﺎﻜﯾﺮﻣﻷا - AND نﺎﻜﯾﺮﻣا - AND نﺎﻜﯾﺮﻣأ - AND - AND ﻣﻷ ﯿ ﻛﺮ ﺎ ن - AND ﯾﺮﻣﻼﻟ ﻜ نﺎ - AND ﯾﺮﻣﻸﻟ ﻜ نﺎ - AND ﺮﻣﻻ ﯾ ﻜ ﺎ ن - AND ﺮﻣﻷ ﯾ ﻜ ﺎ ن - AND نﺎﻛﺮﯿﻣﻻا - AND نﺎﻜﯾﺮﻣﻻﺎﺑ - AND نﺎﻜﯾﺮﻣﻷﺎﺑ - AND نﺎﻜﯾﺮﻣﺎﺑ - AND نﺎﻜﯾﺮﻣﺄﺑ - AND ﻛﺮﯿﻣﻼﻟ نﺎ - AND ﻛﺮﯿﻣﻸﻟ نﺎ - AND ﻻ نﺎﻛﺮﯿﻣ - AND نﺎﻜﯾﺮﻣﺎﻓ - AND نﺎﻜﯾﺮﻣﺄﻓ - AND نﺎﻛﺮﯿﻣﻻﺎﺑ - AND نﺎﻛﺮﯿﻣﻷﺎﺑ - AND نﺎﻛﺮﯿﻣﺎﺑ - AND نﺎﻛﺮﯿﻣﺄﺑ - AND نﺎﻜﯾﺮﻣأو - AND نﺎﻛﺮﯿﻣﻻﺎﻓ - AND نﺎﻛﺮﯿﻣﻷﺎﻓ - AND نﺎﻛﺮﯿﻣﺎﻓ - AND نﺎﻛﺮﯿﻣﺄﻓ - AND نﺎﻜﯾﺮﻣﻻﺎﻓ - AND ﻓ ﯾﺮﻣﻷﺎ ﻜ نﺎ ﺮﻷ AND نﺎﻛﺮﯿﻣﻷاو - AND نﺎﻛﺮﯿﻣاو - AND نﺎﻛﺮﯿﻣأو - AND نﺎﻜﯾﺮﻣﻻاو - AND نﺎﻜﯾﺮﻣﻷاو - AND نﺎﻜﯾﺮﻣاو - AND " تﺎﯾﻻﻮﻟﺎﻓ ةﺪﺤﺘﻤﻟا "- AND " تﺎﯾﻻﻮﻟﺎﺑ ةﺪﺤﺘﻤﻟا "- AND " تﺎﯾﻻﻮﻠﻟ ةﺪﺤﺘﻤﻟا "- AND " تﺎﯾﻻﻮﻟا ةﺪﺤﺘﻤﻟا "- AND نﺎﻛﺮﯿﻣﻻاو - وو ﻦﻄﻨﺷا - AND ﻦﻄﻨﺷاﻮﻓ - AND ﻦﻄﻨﺷاﻮﺑ - AND ﻦﻄﻨﺷاﻮﻟ - AND ﻦﻄﻨﺷاو - AND " او تﺎﯾﻻﻮﻟ ا ةﺪﺤﺘﻤﻟ "- AND ﺑوا ﺎﻣﺎ - AND ﺎﻣﺎﺑوﺄﺑ - AND ﺑﻻ ﺎ ﻣ ﺎ - AND وﻻ ﺑ ﺎ ﻣ ﺎ - AND وﻷ ﺑ ﺎ ﻣ ﺎ - AND ﺎﻣﺎﺑا - AND ﺑوا ﺎﻣﺎ - AND ﺑوأ ﺎﻣﺎ - AND AND بﺮﻐﻟا - AND ﺎﻣﺎﺑا - AND ﺑوا ﺎﻣﺎ - AND ﺑوأ ﺎﻣﺎ - AND ﺎﻣﺎﺑا - AND ﺑوا ﺎﻣﺎ - AND ﺑوأ ﺎﻣﺎ - AND ﺎﻣﺎﺑا - AND ﻲﺑﺮﻐﻠﻟ - AND ﻲﺑﺮﻐﻟ - AND ﻲﺑﺮﻐﻟا - AND ﺮﻏ ﺑ ﻲ - AND او بﺮﻐﻟ - AND بﺮﻐﻟﺎﻓ - AND بﺮﻐﻟﺎﺑ - AND بﺮﻐﻠﻟ - AND ﺮﻏ ﺑ ﯿ ﺔ - AND او ﻲﺑﺮﻐﻟ - AND ﺮﻏو ﻲﺑ - AND ﻲﺑﺮﻐﻟﺎﻓ - AND ﻲﺑﺮﻐﻓ - AND ﻲﺑﺮﻐﻟﺎﺑ - AND ﻲﺑﺮﻐﺑ - AND ﺮﻏو ﺑ ﯿ ﺔ - AND ﺔﯿﺑﺮﻐﻟﺎﻓ - AND ﺑﺮﻐﻓ ﺔﯿ - AND ﺔﯿﺑﺮﻐﻟﺎﺑ - AND ﺔﯿﺑﺮﻐﺑ - AND ﺔﯿﺑﺮﻐﻠﻟ - AND ﺔﯿﺑﺮﻐﻟ - AND ﺔﯿﺑﺮﻐﻟا - AND " ﺖﯿﺒﻠﻟ ﺾﯿﺑﻷا "- AND " ﺖﯿﺒﻠﻟ ﺾﯿﺑﻻا "- AND " ﺖﯿﺒﻟا ﺾﯿﺑﻷا "- AND " ﺖﯿﺒﻟا ﺾﯿﺑﻻا "- AND او ﺔﯿﺑﺮﻐﻟ - AND " او ﻟ ﺒ ﺖﯿ ﺾﯿﺑﻻا "- AND " ﺖﯿﺒﻟﺎﻓ ﺾﯿﺑﻷا "- AND " ﺖﯿﺒﻟﺎﻓ ﺾﯿﺑﻻا "- AND " ﺖﯿﺒﻟﺎﺑ ﺾﯿﺑﻷا "- AND " ﺖﯿﺒﻟﺎﺑ ﺾﯿﺑﻻا "- ﻲﺸﻋاد - AND دو ﺶﻋا - AND ﺶﻋاﺪﻟ - AND ﺶﻋاﺪﻓ - AND ﺶﻋاﺪﺑ - AND ﺶﻋاد - AND " او ﻟ ﺒ ﺖﯿ ﺾﯿﺑﻷا "- AND ﺶﻋاوﺪﺑ - AND ود ﺶﻋا - AND ﮫﯿﺸﻋاﺪﻟا - AND ﮫﯿﺸﻋاد - AND ﺔﯿﺸﻋاﺪﻟا - AND ﺔﯿﺸﻋاد - AND ﻲﺸﻋاﺪﻟا - AND - AND ﺶﻋاوﺪﻟﺎﻓ - AND ﺶﻋاوﺪﻟﺎﺑ - AND ﺶﻋاوﺪﻟا - AND ودو ﺶﻋا - AND ﺶﻋاوﺪﻟ - AND ﺶﻋاوﺪﻓ - AND - AND " ﺔﻟوﺪﻟﺎﻓ ﻼﺳﻹا ﺔﯿﻣ "- AND " ﺔﻟوﺪﻟﺎﺑ ﺔﯿﻣﻼﺳﻹا "- AND " ﺔﻟوﺪﻟا ﺔﯿﻣﻼﺳﻹا "- AND او ﺶﻋاوﺪﻟ - AND ﻟ ﺶﻋاوﺪﻠ ﺔﻟوﺪﻟﺎﻓ "- AND " ﺔﻟوﺪﻟﺎﺑ ﺔﯿﻣﻼﺳﻻا "- AND " ﺔﻟوﺪﻟا ﺔﯿﻣﻼﺳﻻا "- AND " او ﺔﻟوﺪﻟ ﺔﯿﻣﻼﺳﻹا "- AND " ﺔﻟوﺪﻠﻟ ﺔﯿﻣﻼﺳﻹا " " ﮫﻟوﺪﻟﺎﺑ ﮫﯿﻣﻼﺳﻹا "- AND " ﮫﻟوﺪﻟا ﮫﯿﻣﻼﺳﻹا "- AND " او ﺔﻟوﺪﻟ ﺔﯿﻣﻼﺳﻻا "- AND " ﺔﻟوﺪﻠﻟ ﺔﯿﻣﻼﺳﻻا "- AND " ﯿﻣﻼﺳﻻا ﺔﯿﻼﻻ - AND " ﮫﻟوﺪﻟا ﮫﯿﻣﻼﺳﻻا "- AND " او ﮫﻟوﺪﻟ ﮫﯿﻣﻼﺳﻹا "- AND " ﮫﻟوﺪﻠﻟ ﮫﯿﻣﻼﺳﻹا "- AND " ﮫﻟوﺪﻟﺎﻓ ﮫﯿﻣﻼﺳﻹا "- AND - AND " او ﮫﻟوﺪﻟ ﮫﯿﻣﻼﺳﻻا "- AND " ﮫﻟوﺪﻠﻟ ﮫﯿﻣﻼﺳﻻا "- AND " ﮫﻟوﺪﻟﺎﻓ ﮫﯿﻣﻼﺳﻻا "- AND " ﮫﻟوﺪﻟﺎﺑ ﮫﯿﻣﻼﺳﻻا " - AND " ﻹا_ﺔﻟوﺪﻠﻟ ﻼﺳ ﻣ ﯿ ﺔ "- AND " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟﺎﻓ "- AND " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟﺎﺑ "- AND " ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟا " AND " ﺗو ﻢﯿﻈﻨ ا ﻟ ﺔﻟوﺪ "- AND " ﻢﯿﻈﻨﺘﻟ ﺔﻟوﺪﻟا "- AND " ﻢﯿﻈﻨﺘﺑ ﺔﻟوﺪﻟا "- AND " ﻢﯿﻈﻨﺗ ﺔﻟوﺪﻟا "- AND " او ﺔﯿﻣﻼﺳﻹا_ﺔﻟوﺪﻟ " - AND " ﺗو ﻢﯿﻈﻨ ا ﻟ ﮫﻟوﺪ "- AND " ﻢﯿﻈﻨﺘﻟ ﮫﻟوﺪﻟا "- AND " ﻢﯿﻈﻨﺘﺑ ﮫﻟوﺪﻟا "- AND " ﻢﯿﻈﻨﺗ ﮫﻟوﺪﻟا "- AND " ﻢﯿﻈﻨﺘﻓ ﺔﻟوﺪﻟا "- - AND " ودو ﻟ ﺔ ا ﺔﻓﻼﺨﻟ "- AND " ﺔﻟوﺪﻟ ا ﺔﻓﻼﺨﻟ "- AND " ﺔﻟوﺪﺑ ا ﺔﻓﻼﺨﻟ "- AND " ود ﻟ ﺔ ﺔﻓﻼﺨﻟا "- AND " ﻢﯿﻈﻨﺘﻓ ﮫﻟوﺪﻟا " ﺒﺧأ رﺎ "- AND او ﻟ يداﺪﻐﺒ - AND يداﺪﻐﺒﻟﺎﻓ - AND يداﺪﻐﺒﻠﻟ - AND يداﺪﻐﺒﻟﺎﺑ - AND يداﺪﻐﺒﻟا - AND " ﺔﻟوﺪﻓ ا ﺔﻓﻼﺨﻟ " " رﺎﺒﺧﺄﻓ ﺔﻓﻼﺨﻟا "- AND " رﺎﺒﺧأو ا ﺔﻓﻼﺨﻟ "- AND " ﺧﻷ ﺒ ﺎ ر ا ﻟ ﻼﺨ ﻓ ﺔ "- AND " رﺎﺒﺧﺄﺑ ﺔﻓﻼﺨﻟا "- AND " ﺔﻓﻼﺨﻟا

26 6.4 The Salience of International Politics Hypothesis

Perhaps the salience of international politics constitutes an omitted variable explaining our correlation. According to this interpretation, those who Tweet negative sentiments towards the US do so because issues of Middle Eastern international politics are more salient to them. Those who do not Tweet such sentiments may still feel just as negatively towards the US; however, because issues of Middle Eastern international politics are less salient for them, they do not express such sentiments, only Tweeting neutral news articles about the US. Thus, if the salience of international politics co-varies with negativity towards ISIS and the US, then perhaps it is individuals’ interest in international politics that explains our relationship.

One implication of this hypothesis is that those who are more negative about ISIS will post more often about the US. This assumes that frequency of posting about the US is an indication of the salience of international politics to a given user. We calculated the average number of posts per user in the US Analysis for each of our 16 groups of Tweeters, to see if there was a positive relation-ship between negativity towards ISIS and how much you post about the US. The results are mixed but overall provide evidence against this explanation. On the one hand, there is a positive relation-ship for some groups. For instance, for the lowest-volume group (1–3 posts in the sample), a move from the lowest to the highest ISIS negativity group corresponds to an increase from 46 to 64 posts about the US per user; the increase for the next volume group (3 to 5 posts in the sample) is similarly small, going from 262 to 287 posts per user. However, the other two groups show a much stronger relationship in the opposite direction. For the next volume group (5 to 10 posts in the sample), going from the lowest to the highest ISIS negativity group corresponds with a large decrease from 592 to 383 posts per user, while the decrease for the highest-volume group (10+ posts in the sample) is an even larger drop from 1,885 to 555 posts per user.

Figure 8 – Relationship between ISIS negativity and number of posts in the US analysis

27 6.5 The Sunni-Shia Hypothesis

One final hypothesis is that the correlation between negativity toward the US and ISIS is driven by Shia Twitter users, who could be both more negative towards the US and ISIS than their Sunni counterparts. Although we cannot measure Sunni/Shia identity at the individual level, we can address this alternative hypothesis at the country level. Our Tweets and analyses can be examined while geographically restricting results to certain countries. For each country, we calculated the correlation between ISIS negativity ratio (using the four levels used throughout the rest of this paper) and the percent negative traffic in the General US analysis. We then looked at whether this correlation was higher for Middle Eastern countries with bigger Shia populations. It is not; on average, countries with small Shia populations have the same correlation as those with high Shia populations. See Figure 9 for the results of this analysis, with a LOESS line showing the pattern for the 17 countries we include in this analysis.

Figure 9 – How correlation varies as a function of the percentage of a country’s population that is Shia Muslim.

28 7 ISIS Attacks

When examining the aggregate-level correlation between negativity toward the US and ISIS, we thought that ISIS-related attacks and killings might be spurring these results, as those condemning the attacks express negativity towards ISIS and those celebrating them express positivity towards ISIS and negativity towards the US. To explore this possibility, we examined how trends in negative traffic in the two monitors appear around the time that ISIS attacks take place. The data con- sists of a list of ISIS-related attacks and killings that took place during our time frame of interest (2014 to 2015), which we gathered to inspect how reactions to these incidents affects traffic in our General US and ISIS analyses. This list was culled from two online sources: (1) a New York Times list of ISIS attacks maintained by three reporters3 and (2) Wikipedia’s lists of non-state terrorist incidents.4 After filtering out non-ISIS attacks, removing attacks that took place in Iraq and Syria,5 and correcting a few of the dates as needed, we combined these lists to come up with a total of 77 events that took place during our time frame of interest. In our analyses, we use these events to examine how results in our monitor vary as a function of ISIS-related attacks and killings

presents these trends for all 77 events in our list (numbered for space considerations, the full list being available in the appendix). Negative traffic in the General ISIS analysis behaves the way you might expect, peaking for several of the most prominent events, such as the killing of the Jordanian Pilot in February 2015 (event 25) or the June 2015 mass shooting at a Tunisian resort (event 50). But negative traffic in the General US analysis barely peaks, if at all, for most of these events. The main exception to this pattern is the November 2015 Paris Attacks (event 72), in which negative political US traffic peaks with negative ISIS traffic. This may indicate that co-occurring negativity is partially a result of large-scale ISIS attacks against the West. However, this explanation is complicated by the lack of negative peaks in reaction to the dates for the San Bernardino attack, where we examined traffic on the date of the attack (event 75) as well as on the date when Obama declared the incident a terror attack (event 76). Additionally, there was no negative reaction in either analysis to the Charlie Hebdo attack (event 16). This seems to indicate that the correlation between negative traffic in the two analyses is not primarily due to reactions to attacks or killings by ISIS, including large-scale attacks against the West.

3 http://www.nytimes.com/interactive/2015/06/17/world/middleeast/map-isis-attacks-around-the-world.html 4 https://en.wikipedia.org/wiki/List_of_non-state_terrorist_incidents 5 We saw such attacks as fundamentally different from attacks outside of ISIS’s warzone, and we decided that they were not relevant to our analysis.

Figure 10 – Trends in negative traffic about ISIS and negative political traffic about the US during time periods where an ISIS-related attack occurred. Each plot shows the 5 days before and after the event. The solid black lines mark the event itself; the dashed lines mark the dates of other events if they fall during the same 11-day period. Note that several events occur on the same date as others, so some of the plots are repeated. The list of events is found in the appendix.

Below is the full list of ISIS-related attacks used in our analysis. Most of the event descriptions are taken from the related Wikipedia articles.

• 01. US civilian 01 o 8/19/14 o James Wright Foley was an American freelance journalist and photojournalist of the Syrian Civil War when he was abducted on November 22, 2012, in northwestern Syria. Foley was the first American citizen to be killed by "Jihadi John." James Foley's beheading by ISIL received wide condemnation in the United States. • 02. Lebanese soldier 01 o 8/28/14 o Lebanese Army Sergeant Ali al-Sayyed was beheaded following his capture by ISIL during the Battle of Arsal. ISIL member Abu Musaab Hafid al-Baghdadi posted pictures of his beheading on Twitter. The beheading sparked public outrage in Lebanon. • 03. US civilian 02 o 9/2/14 o Steven Joel Sotloff was an Israeli-American journalist for Time magazine and The Jerusalem Post, although the Post disavowed any relationship once Sotloff's life was threatened. In 2013, he was kidnapped in Aleppo, Syria. On September 2, 2014, a video was released purporting to show "Jihadi John" beheading Steven Sotloff. • 04. Lebanese soldier 02 o 9/6/14 o Lebanese Army soldier Abbas Medlej is believed to have been beheaded following an attempted escape from his captors. ISIL members claim that he was contained following an escape attempt, where he fired upon his captors, according to comments made by an ISIL Leader on the Turkish Anatolia news channel. Gruesome photos of the dead soldier were posted on several pro-jihadist Twitter accounts on September 6. • 05. UK civilian 01 o 9/13/14 o David Haines was abducted in March 2013 while working in Syria for the humanitarian aid group Agency for Technical Cooperation and Development assessing the Atmeh refugee camp near the Turkish border and the Syrian province of Idlib. A video of the lead-up and aftermath of David Haines' beheading, entitled "A Message to the Allies of America", was released by ISIL on September 13, 2014. • 06. Afghani civilian beheadings o 9/20/14 o On September 20, 2014, local officials in the Ghazni Province of Afghanistan reported that Taliban insurgents from different regions of the country led by camouflaged men wearing black masks had captured several villages, set at least

31 60 homes on fire, killed more than 100 people and beheaded fifteen family members of local police officers. The masked insurgents reportedly carried the black flag of ISIL, openly called themselves soldiers of Da'esh, and did not speak any local languages. • 07. Melbourne police stabbing o 9/23/14 o An 18-year-old ISIS sympathizer was shot dead after stabbing two counterterrorism officers outside a Melbourne police station. • 08. French tourist in Algeria o 9/24/14 o Militants kidnapped and beheaded a French tourist shortly after the Islamic State called on supporters around the world to harm Europeans in retaliation for airstrikes in Iraq and Syria. • 09. UK civilian 02 o 10/3/14 o Alan Henning was a British humanitarian aid worker. Henning was the fourth Western hostage killed by ISIL. Henning was captured during ISIL's occupation of the Syrian city of Al-Dana in December 2013, where he was helping with humanitarian relief. Alan Henning was shown at the end of David Haines's beheading video, released on September 13, 2014, and referred to by "Jihadi John" as the next victim. A video of Henning's beheading was released by ISIL on October 3, 2014. • 10. Montreal car attack o 10/20/14 o A 25-year-old man who had recently adopted radical Islam ran over two soldiers near Montreal, killing one. • 11. Ottawa attack o 10/22/14 o An Islamic convert shot and killed a soldier who was guarding the National War Memorial in Ottawa, stormed Canada's parliament and fired multiple times before being shot and killed. • 12. Queens hatchet attack o 10/23/14 o A hatchet-wielding man charged at four police officers in Queens. ISIS said the attack was the "direct result" of its September call to action. • 13. US civilian 03 o 11/16/14 o Peter Edward Kassig, also known by the name Abdul-Rahman Kassig, which he assumed in captivity, was 26 years old at the time he was beheaded. Kassig worked in Syria and Lebanon as a humanitarian worker. On October 1, 2013, as he was on his way to Deir Ezzour in eastern Syria to deliver food and medical supplies to refugees, Kassig was taken captive by ISIL. On November 16, 2014, ISIL posted a video showing "Jihadi John" standing over a severed human head. The beheading itself was not shown in the video. The White House later confirmed the person killed was Kassig. • 14. Danish man shot, Saudi Arabia

32 o 11/22/14 o A Danish executive was shot in his car. A group of ISIS supporters later claimed responsibility. • 15. Sydney siege o 12/15/14 o A gunman who said he was acting on ISIS's behalf seized 17 hostages in a Sydney cafe. • 16. Charlie Hebdo attack o 1/7/15 o On January 7, 2015 at about 11:30 local time, two brothers, Saïd and Chérif Kouachi, forced their way into the offices of the French satirical weekly newspaper Charlie Hebdo in Paris. Armed with assault rifles and other weapons, they killed 12 people and injured 11 others. • 17. Coptic kidnappings announced o 1/12/15 o ISIS's Tripoli affiliate said they were holding 21 Egyptian Christians captive. • 18. Lebanese army outpost attack o 1/23/15 o ISIS attacked an outpost of the Lebanese Armed Forces. • 19. Japanese civilian 01 o 1/24/15 o Haruna Yukawa was a Japanese national reported to be beheaded in January 2015. In April 2014, he was in Syria where he was captured by the Free Syrian Army; Japanese journalist Kenji Goto was brought in to interpret, and Goto secured Yukawa's release. Both Yukawa and Goto went back to Japan, but Yukawa soon returned to Syria, where he disappeared after July 2014; ISIL released a video on YouTube of Yukawa on the ground bleeding. In October 2014, Goto returned to Syria to try to secure Yukawa's release; he was soon captured. The two appeared in a video in January 2015 in which ISIL gave the Japanese government a deadline of 72 hours for a ransom of $200 million. The deadline passed without fulfillment of the ransom, and a video of Yukawa's beheading was released. • 20. Libya luxury hotel armed assault o 1/27/15 o ISIS's Tripoli affiliate claimed credit for an armed assault on a luxury hotel that killed at least eight people. It was the deadliest attack on Western interests in Libya since the assault on the American diplomatic mission in Benghazi. • 21. Sinai coordinated attacks o 1/29/15 o ISIS's Sinai affiliate claimed responsibility for coordinated bombings that killed 24 soldiers, six police officers and 14 civilians. • 22. Japanese civilian 02 o 1/31/15 o [Continued from event 19 above.] By the end of the month, the group released another video of the beheading of Goto, in which Jihadi John proclaimed to Japanese prime minister Shinzo Abe "because of your reckless decision to take

33 part in an unwinnable war, this knife will not only slaughter Kenji, but will also carry on and cause carnage wherever your people are found. So let the nightmare for Japan begin." • 23. Alleged Egyptian/Israeli spies o 2/1/15 o In February 2015, in response to the buffer zone the Egyptian government placed along the Gaza-Egypt border, ISIL members beheaded 10 men they believed were spies for Mossad and the Egyptian Army. • 24. Libya oil field attack o 2/3/15 o ISIS militants were suspected of killing 12 people, including four foreigners, in an attack on an oil field. • 25. Jordanian pilot o 2/3/15 o https://en.wikipedia.org/wiki/Muath_al-Kasasbeh • 26. US civilian 04 o 2/10/15 o https://en.wikipedia.org/wiki/Kayla_Mueller • 27. Coptic beheading in Libya o 2/15/15 o ISIS released a video that appeared to show its militants in Libya beheading a group of Egyptian Christians who had been kidnapped in January. • 28. Copenhagen shooting o 2/15/15 o A Danish-born gunman who was inspired by ISIS went on a violent rampage in Copenhagen, killing two strangers and wounding five police officers. • 29. Libya car bombings o 2/20/15 o ISIS's Derna affiliate claimed responsibility for three car bombs that killed at least 40 people. • 30. Bardo museum shooting o 3/18/15 o ISIS claimed responsibility for an attack on a museum that killed 22 people, almost all European tourists. • 31. Yemen shiite mosques bombings o 3/20/15 o An ISIS affiliate claimed responsibility for coordinated suicide strikes on Zaydi Shiite mosques that killed more than 130 people during Friday Prayer. • 32. Sinai car bombs at checkpoints o 4/2/15 o Sinai's ISIS affiliate killed 13 people with simultaneous car bombs at military checkpoints. • 33. Kidnapping of Afghani civilians o 4/4/15 o The Afghan vice president accused ISIS of kidnapping 31 civilians in February. • 34. Libyan checkpoint attack

34 o 4/5/15 o ISIS killed at least four people in an attack on a security checkpoint. • 35. Police attack in Saudi Arabia o 4/8/15 o Gunmen opened fire on a police patrol, killing two officers. • 36. Moroccan embassy in Tripoli o 4/12/15 o ISIS's Tripoli affiliate claimed credit for a bomb that exploded outside the Moroccan Embassy. • 37. Mult. attacks, Egyptian sec. forces o 4/12/15 o ISIS militants killed at least 12 people in three separate attacks on Egyptian security forces. • 38. Korean embassy in Tripoli o 4/12/15 o ISIS's Tripoli affiliate claimed responsibility for an attack on the South Korean Embassy that killed two local police officers. • 39. Ethiopian Christians in Libya o 4/19/15 o ISIS released a video of militants from two of its Libya affiliates killing dozens of Ethiopian Christians, some by beheading and others by shooting. • 40. Yemen soldiers killing o 4/30/15 o One of ISIS's Yemen affiliates released a video showing the killing of 15 Yemeni soldiers. • 41. Garland, Texas center shooting o 5/3/15 o Two men who reportedly supported ISIS and were later acknowledged by ISIS as "soldiers of the caliphate" opened fire in a Dallas suburb outside a Prophet Muhammad cartoon contest. • 42. Yemen shiite mosque bombing o 5/22/15 o ISIS claimed responsibility for a bomb attack on a Shiite mosque that injured at least 13 worshipers. • 43. Saudi shiite mosque bombing o 5/22/15 o In what appeared to be ISIS's first official claim of an attack in Saudi Arabia, a suicide bomber detonated an explosive at a Shiite mosque during midday prayer, killing at least 21 and injuring 120. • 44. Saudi mosque suicide bombing o 5/29/15 o One week after a similar attack in the same region, a suicide bomber dressed in women’s clothing detonated an explosive belt near the entrance to a Shiite mosque, killing three people. • 45. Libyan checkpoint bombing o 5/31/15

35 o A suicide bomber from an ISIS affiliate killed at least four Libyan fighters at a checkpoint. • 46. Beheading of Taliban members o 6/3/15 o ISIS is suspected of beheading 10 members of the Taliban. • 47. Kurdish political rally explosion o 6/5/15 o An explosion at a political rally in the predominantly Kurdish city of Diyarbakir killed two people and wounded more than 100. Turkish officials have said ISIS was behind the attack. • 48. Sinai air base bombing o 6/9/15 o ISIS's Sinai province claimed responsibility for firing rockets toward an air base used by an international peacekeeping force. • 49. Yemen car bombings o 6/17/15 o An ISIS branch claimed responsibility for a series of car bombings in Sana, the capital, that killed at least 30 people. • 50. Tunisian hotel mass shooting o 6/26/15 o At least one gunman disguised as a vacationer attacked a Mediterranean resort, killing at least 38 people at a beachfront hotel, most of them British tourists, before he was shot to death by the security forces. • 51. Kuwait mosque bombing o 6/26/15 o A suicide bomber detonated explosives at one of the largest Shiite mosques in Kuwait City during Friday Prayer. • 52. Army and security killings, Egypt o 7/1/15 o Militants affiliated with the Islamic State killed dozens of soldiers in simultaneous attacks on Egyptian Army checkpoints and other security installations in Egypt's northern Sinai Peninsula. • 53. Italian consulate in Egypt o 7/11/15 o ISIS claimed responsibility for an explosion outside the Italian Consulate’s compound in downtown Cairo that killed one person. • 54. Egypt naval vessel o 7/16/15 o In what appeared to be the first attack on a naval vessel claimed by Sinai Province, the ISIS affiliate said it destroyed an Egyptian naval vessel and posted photographs on social media of a missile exploding in a ball of fire as it slammed into the vessel. • 55. Turkey cultural center mass killing o 7/20/15 o A Turkish citizen believed to have had ties to ISIS killed at least 32 people at a cultural center.

36 • 56. Saudi mosque bombing o 8/6/15 o ISIS claimed responsibility for a suicide bombing at a mosque that killed at least 15 people, including 12 members of a Saudi police force. • 57. Egypt Croation beheading o 8/12/15 o An ISIS affiliate said it had beheaded a Croatian expatriate worker because of Croatia's "participation in the war against the Islamic State." • 58. Egypting sec. agency bombing o 8/20/15 o An ISIS affiliate claimed responsibility for bombing a branch of the Egyptian security agency. • 59. Egypt police shooting o 8/26/15 o The Sinai Province of the Islamic State group claimed responsibility for three gunmen who shot and killed two police officers. • 60. Yemen mosque bombings 01 o 9/2/15 o Yemen's ISIS affiliate claimed responsibility for two bombings at a mosque that killed at least 20 people. • 61. Tripoli, Libya prison attack o 9/18/15 o Militants loyal to the Islamic State attacked a prison inside a Tripoli air base. • 62. Yemen mosque bombings 02 o 9/24/15 o At least 25 people were killed when two bombs went off outside a mosque during prayers to commemorate Eid al-Adha, a major Muslim holiday. • 63. Bangladesh aid worker shooting o 9/28/15 o ISIS claimed responsiblity for the shooting death of an Italian aid worker. • 64. Bangladesh rickshaw shooting o 10/3/15 o ISIS claimed responsibilty for the shooting death of a Japanese man riding a rickshaw. • 65. Yemen bombings o 10/6/15 o A series of bombings in Yemen's two largest cities killed at least 25 people. • 66. Ankara bombings o 10/10/15 o Two explosions killed more than 100 people who had gathered for a peace rally in Turkey's capital. Turkish officials believe ISIS is responsible. • 67. Bangladesh shiite bombing o 10/24/15 o ISIS claimed responsiblity for bombings that killed one person and wounded dozens more during a procession commemorating a Shiite Muslim holiday. • 68. Russian jet in Egypt

37 o 10/31/15 o An ISIS affiliate in Sinai claimed responsiblity for the downing of a Russian passenger jet that killed all 224 people on board. • 69. Egypt suicide bombing o 11/4/15 o ISIS's Sinai affiliate claimed responsiblity for a suicide bombing that killed at least four police officers. • 70. Bangladesh police stabbing o 11/4/15 o ISIS claimed responsibility for a stabbing and shooting that left one police officer dead and another wounded. • 71. Lebanon suicide bombings o 11/12/15 o ISIS claimed responsiblity for a double suicide bombing that ripped through a busy shopping district at rush hour, killing at least 43 people. • 72. Multiple Paris attacks o 11/13/15 o President François Hollande blamed the Islamic State for terrorist attacks across Paris that killed more than 100 people. The Islamic State claimed responsiblity. • 73. Sinai hotel, Egypt attack o 11/24/15 o ISIS militants attacked a hotel in the northern Sinai Peninsula, killing at least seven people. • 74. Bangladesh shiite mosque o 11/26/15 o ISIS claimed responsiblity for an attack on a Shiite mosque during evening prayer in which gunmen opened fire on worshipers with machine guns, killing one man and injuring three others. • 75. San Bernadino mass shooting o 12/2/15 o A married couple shot and killed 14 people in San Bernardino, Calif. The FBI is investigating the shooting as an act of terrorism inspired by ISIS. • 76. SB defined as terrorist attack o 12/6/15 o It wasn’t until four days later that the San Bernadino shootings were officially classified as a terrorist attack. • 77. Yemen car bombing o 12/7/15 o The Islamic State claimed responsibility for a car bomb that killed a provincial governor and eight of his body guards.