Declare: Debunking Fake News and False Claims Using Evidence-Aware Deep Learning

DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

Kashyap Popat1, Subhabrata Mukherjee2, Andrew Yates1, Gerhard Weikum1 1Max Planck Institute for Informatics, Saarbrucken,¨ Germany 2Amazon Inc., Seattle, USA {kpopat,ayates,weikum}@mpi-inf.mpg.de, [email protected]

Abstract State of the Art and Limitations: Prior work on “truth discovery” (see Li et al.(2016) for survey) 1 Misinformation such as fake news is one of largely focused on structured facts, typically in the big challenges of our society. Research on the form of subject-predicate-object triples, or on automated fact-checking has proposed meth- social media platforms like Twitter, Sina Weibo, ods based on supervised learning, but these etc. Recently, methods have been proposed to as- approaches do not consider external evidence apart from labeled training instances. Recent sess the credibility of claims in natural language approaches counter this deficit by considering form (Popat et al., 2017; Rashkin et al., 2017; external sources related to a claim. However, Wang, 2017), such as news headlines, quotes from these methods require substantial feature mod- speeches, blog posts, etc. eling and rich lexicons. This paper overcomes The methods geared for general text input ad- these limitations of prior work with an end-to- dress the problem in different ways. On the one end model for evidence-aware credibility as- hand, methods like Rashkin et al.(2017); Wang sessment of arbitrary textual claims, without any human intervention. It presents a neural (2017) train neural networks on labeled claims network model that judiciously aggregates sig- from sites like PolitiFact.com, providing credibil- nals from external evidence articles, the lan- ity assessments without any explicit feature mod- guage of these articles and the trustworthiness eling. However, they use only the text of ques- of their sources. It also derives informative tionable claims and no external evidence or inter- features for generating user-comprehensible actions that provide limited context for credibil- explanations that makes the neural network ity analysis. These approaches also do not offer predictions transparent to the end-user. Exper- any explanation of their verdicts. On the other iments with four datasets and ablation studies show the strength of our method. hand, Popat et al.(2017) considers external evidence in the form of other articles (retrieved from 1 Introduction the Web) that confirm or refute a claim, and jointly assesses the language style (using subjectivity lex- Motivation: Modern media (e.g., news feeds, mi- icons), the trustworthiness of the sources, and the croblogs, etc.) exhibit an increasing fraction of credibility of the claim. This is achieved via a misleading and manipulative content, from ques- pipeline of supervised classifiers. On the upside, tionable claims and “alternative facts” to com- this method generates user-interpretable explana- pletely faked news. The media landscape is be- tions by pointing to informative snippets of evi- coming a twilight zone and battleground. This so- dence articles. On the downside, it requires sub- cietal challenge has led to the rise of fact-checking stantial feature modeling and rich lexicons to de- and debunking websites, such as Snopes.com tect bias and subjectivity in the language style. and PolitiFact.com, where people research claims, Approach and Contribution: To overcome the manually assess their credibility, and present their limitations of the prior works, we present De- verdict along with evidence (e.g., background ar- ClarE2, an end-to-end neural network model for ticles, quotations, etc.). However, this manual ver- assessing and explaining the credibility of arbi- ification is time-consuming. To keep up with the 1As fully objective and unarguable truth is often elusive scale and speed at which misinformation spreads, or ill-defined, we use the term credibility rather than “truth”. we need tools to automate this debunking process. 2Debunking Claims with Interpretable Evidence

22 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 22–32 Brussels, Belgium, October 31 - November 4, 2018. c 2018 Association for Computational Linguistics trary claims in natural-language text form. Our 2 End-to-end Framework for Credibility approach combines the best of both families of Analysis prior methods. Similar to Popat et al.(2017), De- ClarE incorporates external evidence or counter- Consider a set of N claims hCni from the respec- evidence from the Web as well as signals from the tive origins/sources hCSni, where n ∈ [1,N]. language style and the trustworthiness of the un- Each claim Cn is reported by a set of M arti- derlying sources. However, our method does not cles hAm,ni along with their respective sources require any feature engineering, lexicons, or other hASm,ni, where m ∈ [1,M]. Each corresponding manual intervention. Rashkin et al.(2017); Wang tuple of claim and its origin, reporting articles and (2017) also develop an end-to-end model, but De- article sources – hCn,CSn,Am,n, ASm,ni forms ClarE goes far beyond in terms of considering ex- a training instance in our setting, along with the ternal evidence and joint interactions between sev- credibility label of the claim used as ground-truth eral factors, and also in its ability to generate user- during network training. Figure1 gives a pictorial interpretable explanations in addition to highly overview of our model. In the following sections, accurate assessments. For example, given the we provide a detailed description of our approach. “the gun epidemic natural-language input claim 2.1 Input Representations is the leading cause of death of young African- American men, more than the next nine causes put The input claim Cn of length l is represented as d together” by Hillary Clinton, DeClarE draws on [c1, c2, ..., cl] where cl ∈ < is the d-dimensional evidence from the Web to arrive at its verdict cred- word embedding of the l-th word in the input ible, and returns annotated snippets like the one claim. The source/origin of the claim CSn is rep- in Table6 as explanation. These snippets, which resented by a ds-dimensional embedding vector ds contain evidence in the form of statistics and as- csn ∈ < . sertions, are automatically extracted from web ar- A reporting article Am,n consisting of k to- ticles from sources of varying credibility. kens is represented by [am,n,1, am,n,2, ..., am,n,k], d Given an input claim, DeClarE searches for web where am,n,k ∈ < is the d-dimensional word articles related to the claim. It considers the con- embedding vector for the k-th word in the report- text of the claim via word embeddings and the ing article Am,n. The claim and article word em- (language of) web articles captured via a bidirec- beddings have shared parameters. The source of tional LSTM (biLSTM), while using an attention the reporting article ASm,n is represented as a ds- ds mechanism to focus on parts of the articles accord- dimensional vector, asm,n ∈ < . For the sake ing to their relevance to the claim. DeClarE then of brevity, we drop the notation subscripts n and aggregates all the information about claim source, m in the following sections by considering only a web article contexts, attention weights, and trust- single training instance – the input claim Cn from worthiness of the underlying sources to assess the source CSn, the corresponding article Am,n and claim. It also derives informative features for in- its sources ASm,n given by: hC, CS, A, ASi. terpretability, like source embeddings that capture 2.2 Article Representation trustworthiness and salient words captured via attention. Key contributions of this paper are: To create a representation of an article, which may capture task-specific features such as whether it • Model: An end-to-end neural network model contains objective language, we use a bidirectional which automatically assesses the credibility Long Short-Term Memory (LSTM) network as of natural-language claims, without any hand- proposed by Graves et al.(2005). A basic LSTM crafted features or lexicons. cell consists of various gates to control the flow of • Interpretability: An attention mechanism in information through timesteps in a sequence, mak- our model that generates user-comprehensible ing LSTMs suitable for capturing long and short explanations, making credibility verdicts range dependencies in text that may be difficult transparent and interpretable. to capture with standard recurrent neural networks • Experiments: Extensive experiments on four (RNNs). Given an input word embedding of to- datasets and ablation studies, demonstrating kens haki, an LSTM cell performs various non- effectiveness of our method over state-of-the- linear transformations to generate a hidden vector art baselines. state hk for each token at each timestep k.

23 Claim Word Claim Source Embeddings Embedding Concatenate Dense Layer

avg Attention Weights

Softmax X avg Credibility X Score

Article Word Inner Product Softmax/ Embeddings Linear

Bidirectional Dense Dense LSTM Layer Layer Article Source Concatenate Embedding Features

Figure 1: Framework for credibility assessment. Upper part of the pipeline combines the article and claim embeddings to get the claim speciﬁc attention weights. Lower part of the pipeline captures the article representation through biLSTM. Attention focused article representation along with the source embeddings are passed through dense layers to predict the credibility score of the claim.

We use bidirectional LSTMs in place of stan- words therein: dard LSTMs. Bidirectional LSTMs capture both 1 X the previous timesteps (past features) and the fu- c¯ = cl l ture timesteps (future features) via forward and l backward states respectively. Correspondingly, We combine this overall representation of the there are two hidden states that capture past and claim with each article term: future information that are concatenated to form −→ ←− the ﬁnal output as: hk = [hk, hk]. aˆk = ak ⊕ c¯

d+d where, aˆk ∈ < and ⊕ denotes the concatenate 2.3 Claim Specific Attention operation. We then perform a transformation to obtain claim-specific representations of each arti- As we previously discussed, it is important to con- cle term: sider the relevance of an article with respect to the claim; specifically, focusing or attending to parts 0 ak = f(Waaˆk + ba) of the article that discuss the claim. This is in con- trast to prior works (Popat et al., 2017; Rashkin where Wa and ba are the corresponding weight et al., 2017; Wang, 2017) that ignore either the ar- matrix and bias terms, and f is an activation func- ticle or the claim, and therefore miss out on this tion3, such as ReLU, tanh, or the identity func- important interaction. tion. Following this, we use a softmax activation We propose an attention mechanism to help our to calculate an attention score αk for each word model focus on salient words in the article with in the article capturing its relevance to the claim respect to the claim. To this end, we compute context: the importance of each term in an article with 0 exp(ak) respect to an overall representation of the corre- αk = P 0 (1) k exp(ak) sponding claim. Additionally, incorporating attention helps in making our model transparent and in- 2.4 Per-Article Credibility Score of Claim terpretable, because it provides a way to generate Now that we have article term representations the most salient words in an article as evidence of given by hhki and their relevance to the claim our model’s verdict. given by hαki, we need to combine them to pre- Following Wieting et al.(2015), the overall rep- dict the claim’s credibility. In order to create an resentation of an input claim is generated by tak- 3In our model, the tanh activation function gives best re- ing an average of the word embeddings of all the sults.

24 attention-focused representation of the article con- 3.1 Snopes sidering both the claim and the article’s language, Snopes (www.snopes.com) is a general fact- we calculate a weighted average of the hidden checking website where editors manually investi- state representations for all article tokens based on gate various kinds of rumors reported on the In- their corresponding attention scores: ternet. We used the Snopes dataset provided by Popat et al.(2017). This dataset consists of ru- 1 X g = αk · hk (2) mors analyzed on the Snopes website along with k k their credibility labels (true or false), sets of reporting articles, and their respective web sources. We then combine all the different feature representations: the claim source embedding (cs), the 3.2 PolitiFact attention-focused article representation (g), and PolitiFact is a political fact-checking website the article source embedding (as). In order to (www.politifact.com) in which editors rate merge the different representations and capture the credibility of claims made by various politi- their joint interactions, we process them with two cal figures in US politics. We extract all articles fully connected layers with non-linear activations. from PolitiFact published before December 2017. Each article includes a claim, the speaker (polit- d = relu(W (g ⊕ cs ⊕ as) + b ) 1 c c ical figure) who made the claim, and the claim’s d2 = relu(Wdd1 + bd) credibility rating provided by the editors. PolitiFact assigns each claim to one of six pos- where, W and b are the corresponding weight ma- sible ratings: true, mostly true, half true, mostly trix and bias terms. false, false and pants-on-fire. Following Rashkin Finally, to generate the overall credibility label et al.(2017), we combine true, mostly true and of the article for classification tasks, or credibil- half true ratings into the class label true and the ity score for regression tasks, we process the final rest as false – hence considering only binary cred- representation with a final fully connected layer: ibility labels. To retrieve the reporting articles for each claim (similar to Popat et al.(2017)), we is- s = sigmoid(d ) Classification: 2 (3) sue each claim as a query to a search engine4 and Regression: s = linear(d2) (4) retrieve the top 30 search results with their respective web sources. 2.5 Credibility Aggregation 3.3 NewsTrust The credibility score in the above step is obtained considering a single reporting article. As previ- NewsTrust is a news review community in which ously discussed, we have M reporting articles per members review the credibility of news articles. claim. Therefore, once we have the per-article We use the NewsTrust dataset made available by credibility scores from our model, we take an av- Mukherjee and Weikum(2015). This dataset con- erage of these scores to generate the overall credi- tains NewsTrust stories from May 2006 to May bility score for the claim: 2014. Each story consists of a news article along with its source, and a set of reviews and ratings by 1 X cred(C) = s (5) community members. NewsTrust aggregates these M m m ratings and assigns an overall credibility score (on a scale of 1 to 5) to the posted article. We map the This aggregation is done after the model is attributes in this data to the inputs expected by De- trained. ClarE as follows: the title and the web source of the posted (news) article are mapped to the input 3 Datasets claim and claim source, respectively. Reviews and their corresponding user identities are mapped to We evaluate our approach and demonstrate its gen- reporting articles and article sources, respectively. erality by performing experiments on four differ- We use this dataset for the regression task of pre- ent datasets: a general fact-checking website, a po- dicting the credibility score of the posted article. litical fact-checking website, a news review community, and a SemEval Twitter rumour dataset. 4We use the Bing search API.

25 Dataset SN PF NT SE Parameter SN PF NT SE Total claims 4341 3568 5344 272 Word embedding length 100 100 300 100 True claims 1164 1867 - 127 Claim source embedding length - 4 8 4 False claims 3177 1701 - 50 Article source embedding length 8 4 8 4 Unveriﬁed claims - - - 95 LSTM size (for each pass) 64 64 64 16 Claim sources - 95 161 10 Size of fully connected layers 32 32 64 8 Dropout 0.5 0.5 0.3 0.3 Articles 29242 29556 25128 3717 Article sources 336 336 251 89 Table 2: Model parameters used for each dataset Table 1: Data statistics (SN: Snopes, PF: Politi- (SN: Snopes, PF: PolitiFact, NT: NewsTrust, SE: Fact, NT: NewsTrust, SE: SemEval). SemEval).

3.4 SemEval-2017 Task 8 mance on a withheld dataset. We discard all articles related to Snopes and PolitiFact websites from As the fourth dataset, we consider the benchmark our datasets to have an unbiased model. Statis- dataset released by SemEval-2017 for the task of tics of the datasets after pre-processing is pro- determining credibility and stance of social media vided in Table1. All the datasets are made pub- content (Twitter) (Derczynski et al., 2017). The licly available at https://www.mpi-inf. objective of this task is to predict the credibility mpg.de/dl-cred-analysis/. of a questionable tweet (true, false or unverified) along with a confidence score from the model. It 4 Experiments has two sub-tasks: (i) a closed variant in which models only consider the questionable tweet, and We evaluate our approach by conducting experi- (ii) an open variant in which models consider both ments on four datasets, as described in the previ- the questionable tweet and additional context con- ous section. We describe our experimental setup sisting of snapshots of relevant sources retrieved and report our results in the following sections. immediately before the rumor was reported, a 4.1 Experimental Setup snapshot of an associated Wikipedia article, news articles from digital news outlets, and preceding When using the Snopes, PolitiFact and NewsTrust tweets about the same event. Testing and devel- datasets, we reserve 10% of the data as valida- opment datasets provided by organizers have 28 tion data for parameter tuning. We report 10-fold tweets (1021 reply tweets) and 25 tweets (256 re- cross validation results on the remaining 90% of ply tweets), respectively. the data; the model is trained on 9-folds and the remaining fold is used as test data. When us- 3.5 Data Processing ing the SemEval dataset, we use the data splits In order to have a minimum support for training, provided by the task’s organizers. The objective claim sources with less than 5 claims in the dataset for Snopes, PolitiFact and SemEval experiments is are grouped into a single dummy claim source, binary (credibility) classification, while for New- and article sources with less than 10 articles are sTrust the objective is to predict the credibility grouped similarly (5 articles for SemEval as it is a score of the input claim on a scale of 1 to 5 (i.e., smaller dataset). credibility regression). We represent terms us- For Snopes and PolitiFact, we need to extract ing pre-trained GloVe Wikipedia 6B word embed- relevant snippets from the reporting articles for dings (Pennington et al., 2014). Since our train- a claim. Therefore, we extract snippets of 100 ing datasets are not very large, we do not tune the words from each reporting article having the maxi- word embeddings during training. The remaining mum relevance score: sim = simbow×simsemantic model parameters are tuned on the validation data; where simbow is the fraction of claim words that the parameters chosen are reported in Table2. We are present in the snippet, and simsemantic repre- use Keras with a Tensorflow backend to imple- sents the cosine similarity between the average ment our system. All the models are trained using of claim word embeddings and snippet word em- Adam optimizer (Kingma and Ba, 2014) (learn- beddings. We also enforce a constraint that the ing rate: 0.002) with categorical cross-entropy loss sim score is at least δ. We varied δ from 0.2 for classification and mean squared error loss for to 0.8 and found 0.5 to give the optimal perfor- regression task. We use L2-regularizers with the

26 True Claims False Claims Macro Dataset Conﬁguration AUC Accuracy (%) Accuracy (%) F1-Score LSTM-text 64.65 64.21 0.66 0.70 CNN-text 67.15 63.14 0.66 0.72 Distant Supervision 83.21 80.78 0.82 0.88 Snopes DeClarE (Plain) 74.37 78.57 0.78 0.83 DeClarE (Plain+Attn) 78.34 78.91 0.79 0.85 DeClarE (Plain+SrEmb) 77.43 79.80 0.79 0.85 DeClarE (Full) 78.96 78.32 0.79 0.86 LSTM-text 63.19 61.96 0.63 0.66 CNN-text 63.67 63.31 0.64 0.67 Distant Supervision 62.53 62.08 0.62 0.68 PolitiFact DeClarE (Plain) 62.67 69.05 0.66 0.70 DeClarE (Plain+Attn) 65.53 68.49 0.66 0.72 DeClarE (Plain+SrEmb) 66.71 69.28 0.67 0.74 DeClarE (Full) 67.32 69.62 0.68 0.75

Table 3: Comparison of various approaches for credibility classification on Snopes and PolitiFact datasets. fully connected layers as well as dropout. For all shown in Table3. DeClarE outperforms LSTM- the datasets, the model is trained using each claim- text and CNN-text models by a large margin on article pair as a separate training instance. both datasets. On the other hand, for the Snopes To evaluate and compare the performance of dataset, performance of DeClarE (Full) is slightly DeClarE with other state-of-the-art methods, we lower than the Distant Supervision configuration report the following measures: (p-value of 0.04 with a pairwise t-test). How- • Credibility Classification (Snopes, PolitiFact ever, the advantage of DeClarE over Distant Su- and SemEval): accuracy of the models in clas- pervision approach is that it does not rely on hand sifying true and false claims separately, macro crafted features and lexicons, and can generalize F1-score and Area-Under-Curve (AUC) for well to arbitrary domains without requiring any the ROC (Receiver Operating Characteristic) seed vocabulary. It is also to be noted that both of curve. these approaches use external evidence in the form of reporting articles discussing the claim, which • Credibility Regression (NewsTrust): Mean are not available to the LSTM-text and CNN-text Square Error (MSE) between the predicted and baselines. This demonstrates the value of external true credibility scores. evidence for credibility assessment. 4.2 Results: Snopes and Politifact On the PolitiFact dataset, DeClarE outperforms all the baseline models by a margin of 7-9% We compare our approach with the following AUC (p-value of 9.12e−05 with a pairwise t-test) state-of-the-art models: (i) LSTM-text, a recent with similar improvements in terms of Macro F1. approach proposed by Rashkin et al.(2017). (ii) A performance comparison of DeClarE’s various CNN-text: a CNN based approach proposed by configurations indicates the contribution of each Wang(2017). (iii) Distant Supervision: state- component of our model, i.e, biLSTM capturing of-the-art distant supervision based approach pro- article representations, attention mechanism and posed by Popat et al.(2017). (iv) DeClare source embeddings. The additions of both the (Plain): our approach with only biLSTM (no at- attention mechanism and source embeddings im- tention and source embeddings). (v) DeClarE prove performance over the plain configuration in (Plain+Attn): our approach with only biLSTM all cases when measured by Macro F1 or AUC. and attention (no source embeddings). (vi) De- ClarE (Plain+SrEmb): our approach with only 4.3 Results: NewsTrust biLSTM and source embeddings (no attention). (vii) DeClarE (Full): end-to-end system with biL- When performing credibility regression on the STM, attention and source embeddings. NewsTrust dataset, we evaluate the models in The results when performing credibility classi- terms of mean squared error (MSE; lower is bet- fication on the Snopes and PolitiFact datasets are ter) for credibility rating prediction. We use the

27 Configuration MSE Macro Configuration RMSE Accuracy CNN-text 0.53 CCRF+SVR 0.36 IITP (Open) 0.39 0.746 LSTM-text 0.35 NileTMRG (Close) 0.54 0.673 DistantSup 0.35 DeClarE (Plain) 0.46 0.687 DeClarE (Plain) 0.34 DeClarE (Full) 0.57 0.604 DeClarE (Full) 0.29 Table 5: Comparison of various approaches for Table 4: Comparison of various approaches for credibility classification on SemEval dataset. credibility regression on NewsTrust dataset. 5 Discussion first three models described in Section 4.2 as base- 5.1 Analyzing Article Representations lines. For CNN-text and LSTM-text, we add a linear fully connected layer as the final layer of the In order to assess how our model separates articles model to support regression. Additionally, we also reporting false claims from those reporting true consider the state-of-the-art CCRF+SVR model ones, we employ dimensionality reduction using based on Continuous Conditional Random Field Principal Component Analysis (PCA) to project (CCRF) and Support Vector Regression (SVR) the article representations (g in Equation2) from proposed by Mukherjee and Weikum(2015). The a high dimensional space to a 2d plane. The pro- results are shown in Table4. We observe that De- jections are shown in Figure 2a. We observe that ClarE (Full) outperforms all four baselines, with DeClarE obtains clear separability between credi- a 17% decrease in MSE compared to the best- ble versus non-credible articles in Snopes dataset. performing baselines (i.e., LSTM-text and Dis- 5.2 Analyzing Source Embeddings tant Supervision). The DeClarE (Plain) model performs substantially worse than the full model, Similar to the treatment of article representations, illustrating the value of including attention and we perform an analysis with the claim and arti- source embeddings. CNN-text performs substan- cle source embeddings by employing PCA and tially worse than the other baselines. plotting the projections. We sample a few popu- lar news sources from Snopes and claim sources 4.4 Results: SemEval from PolitiFact. These news sources and claim sources are displayed in Figure 2b and Figure 2c, On the SemEval dataset, the objective is to per- respectively. From Figure 2b we observe that form credibility classification of a tweet while also DeClarE clearly separates fake news sources like producing a classification confidence score. We nationalreport, empirenews, huzlers, etc. from compare the following approaches and consider mainstream news sources like nytimes, cnn, wsj, both variants of the SemEval task: (i) NileTMRG foxnews, washingtonpost, etc. Similarly, from Fig- (Enayet and El-Beltagy, 2017): the best perform- ure 2c we observe that DeClarE locates politicians ing approach for the close variant of the task, (ii) with similar ideologies and opinions close to each IITP (Singh et al., 2017): the best performing ap- other in the embedding space. proach for the open variant of the task, (iii) De- Clare (Plain): our approach with only biLSTM 5.3 Analyzing Attention Weights (no attention and source embeddings), and (iv) Attention weights help understand what DeClarE DeClarE (Full): our end-to-end system with biL- focuses on during learning and how it affects its STM, attention and source embeddings. decisions – thereby, making our model transparent We use the evaluation measure proposed by the to the end-users. Table6 illustrates some interest- task’s organizers: macro F1-score for overall clas- ing claims and salient words (highlighted) that De- sification and Root-Mean-Square Error (RMSE) ClarE focused on during learning. Darker shades over confidence scores. Results are shown in Ta- indicate higher weights given to the corresponding ble5. We observe that DeClarE (Full) outperforms words. As illustrated in the table, DeClarE gives all the other approaches — thereby, re-affirming more attention to important words in the report- its power in harnessing external evidence. ing article that are relevant to the claim and also

28 cnn bernie sanders barack obama nytimes washingtonpost ted cruz hillary clinton rudy giuliani dailycurrant huzlers mike pence bbc paul ryan empirenews foxnews nationalreport usatoday donald trump worldnewsdailyreport wsj mitch mcconnell

(a) Projections of article representations (b) Projections of article source repre- (c) Projections of claim source repre- using PCA; DeClarE obtains clear sep- sentations using PCA; DeClarE clearly sentations using PCA; DeClarE clusters aration between representations of non- separates fake news sources from au- politicians of similar ideologies close to credible articles (red) vs. true ones thentic ones. each other in the embedding space. (green).

Figure 2: Dissecting the article, article source and claim source representations learned by DeClarE.

Table 6: Interpretation via attention (weights) ([True]/[False] indicates the verdict from DeClarE). play a major role in deciding the corresponding guage features and the stance of articles to as- claim’s credibility. In the ﬁrst example on Table6, sess the credibility of the natural language claims. highlighted words such as “..barely true...” and However, their model heavily relies on hand- “..sketchy evidence...” help our system to identify crafted language features. Rashkin et al.(2017); the claim as not credible. On the other hand, high- Wang(2017) propose neural network based ap- lighted words in the last example, like, “..reveal...” proaches for determining the credibility of a tex- and “..documenting reports...” help our system to tual claim, but it does not consider external assess the claim as credible. sources like web evidence and claim sources. These can be important evidence sources for cred- 6 Related Work ibility analysis. The method proposed by Samadi Our work is closely related to the following areas: et al.(2016) uses the Probabilistic Soft Logic Credibility analysis of Web claims: Our work (PSL) framework to estimate source reliability and builds upon approaches for performing credibility claim correctness. Vydiswaran et al.(2011) pro- analysis of natural language claims in an open- poses an iterative algorithm which jointly learns domain Web setting. The approach proposed in the veracity of textual claims and trustworthiness Popat et al.(2016, 2017) employs stylistic lan- of the sources. These approaches do not consider

29 the deeper semantic aspects of language, however. References Wiebe and Riloff(2005); Lin et al.(2011); Re- Carlos Castillo, Marcelo Mendoza, and Barbara casens et al.(2013) study the problem of detecting Poblete. 2011. Information credibility on twitter. In bias in language, but do not consider credibility. Proceedings of the 20th International Conference on World Wide Web, WWW ’11, pages 675–684, New Truth discovery: Prior approaches for truth dis- York, NY, USA. ACM. covery (Yin et al., 2008; Dong et al., 2009, 2015; Li et al., 2011, 2014, 2015; Pasternack and Roth, Leon Derczynski, Kalina Bontcheva, Maria Liakata, 2011, 2013; Ma et al., 2015; Zhi et al., 2015; Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017. Semeval-2017 task 8: Rumoureval: Gao et al., 2015; Lyu et al., 2017) have focused Determining rumour veracity and support for ru- on structured data with the goal of addressing mours. In Proceedings of the 11th International the problem of conflict resolution amongst multi- Workshop on Semantic Evaluation, SemEval@ACL source data. Nakashole and Mitchell(2014) pro- 2017, Vancouver, Canada, August 3-4, 2017, pages 69–76. posed a method to extract conflicting values from the Web in the form of Subject-Predicate-Object Xin Luna Dong, Laure Berti-Equille, and Divesh Sri- (SPO) triplets and uses language objectivity analy- vastava. 2009. Integrating conflicting data: The role of source dependence. Proc. VLDB Endow., sis to determine the true value. Like the other truth 2(1):550–561. discovery approaches, however, this approach is mainly suitable for use with structured data. Xin Luna Dong, Evgeniy Gabrilovich, Kevin Murphy, Van Dang, Wilko Horn, Camillo Lugaresi, Shao- Credibility analysis in social media: Mukher- hua Sun, and Wei Zhang. 2015. Knowledge-based jee et al.(2014); Mukherjee and Weikum(2015) trust: Estimating the trustworthiness of web sources. propose PGM based approaches to jointly in- Proc. VLDB Endow., 8(9):938–949. fer a statement’s credibility and the reliability of Omar Enayet and Samhaa R. El-Beltagy. 2017. sources using language specific features. Ap- Niletmrg at semeval-2017 task 8: Determining ru- proaches like (Castillo et al., 2011; Qazvinian mour and veracity support for rumours on twitter. et al., 2011; Yang et al., 2012; Xu and Zhao, 2012; In Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval@ACL 2017, Van- Gupta et al., 2013; Zhao et al., 2015; Volkova couver, Canada, August 3-4, 2017, pages 470–474. et al., 2017) propose supervised methods for detecting deceptive content in social media plat- Jing Gao, Qi Li, Bo Zhao, Wei Fan, and Jiawei Han. forms like Twitter, Sina Weibo, etc. Similarly, ap- 2015. Truth discovery and crowdsourcing aggregation: A unified perspective. PVLDB, 8(12):2048– proaches like Ma et al.(2016); Ruchansky et al. 2049. (2017) use neural network methods to identify fake news and rumors on social media. Ku- Alex Graves, Santiago Fernandez,´ and Jurgen¨ Schmid- huber. 2005. Bidirectional lstm networks for mar et al.(2016) studies the problem of detect- improved phoneme classification and recognition. ing hoax articles on Wikipedia. All these rely on In Proceedings of the 15th International Con- domain-specific and community-specific features ference on Artificial Neural Networks: Formal like retweets, likes, upvotes, etc. Models and Their Applications - Volume Part II, ICANN’05, pages 799–804, Berlin, Heidelberg. Springer-Verlag. 7 Conclusion Aditi Gupta, Hemank Lamba, Ponnurangam Ku- maraguru, and Anupam Joshi. 2013. Faking sandy: In this work, we propose a completely automated Characterizing and identifying fake images on twit- end-to-end neural network model, DeClarE, for ter during hurricane sandy. In Proceedings of the evidence-aware credibility assessment of natural 22Nd International Conference on World Wide Web, WWW ’13 Companion, pages 729–736, New York, language claims without requiring hand-crafted NY, USA. ACM. features or lexicons. DeClarE captures signals from external evidence articles and models joint Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR, interactions between various factors like the con- abs/1412.6980. text of a claim, the language of reporting articles, and trustworthiness of their sources. Extensive ex- Srijan Kumar, Robert West, and Jure Leskovec. 2016. periments on real world datasets demonstrate our Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes. In Proceed- effectiveness over state-of-the-art baselines. ings of the 25th International Conference on World Wide Web, WWW ’16, pages 591–602, Republic and

30 Canton of Geneva, Switzerland. International World Credibility of user statements in health communi- Wide Web Conferences Steering Committee. ties. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery Qi Li, Yaliang Li, Jing Gao, Lu Su, Bo Zhao, Mu- and Data Mining, KDD ’14, pages 65–74, New rat Demirbas, Wei Fan, and Jiawei Han. 2014. A York, NY, USA. ACM. confidence-aware approach for truth discovery on long-tail data. Proc. VLDB Endow., 8(4):425–436. Ndapandula Nakashole and Tom M. Mitchell. 2014. Language-aware truth assessment of fact candidates. Xian Li, Weiyi Meng, and Clement Yu. 2011. T- In Proceedings of the 52nd Annual Meeting of verifier: Verifying truthfulness of fact statements. the Association for Computational Linguistics, ACL In Proceedings of the 2011 IEEE 27th International 2014, June 22-27, 2014, Baltimore, MD, USA, Vol- Conference on Data Engineering, ICDE ’11, pages ume 1: Long Papers, pages 1009–1019. 63–74, Washington, DC, USA. IEEE Computer So- ciety. Jeff Pasternack and Dan Roth. 2011. Making bet- ter informed trust decisions with generalized fact- Yaliang Li, Jing Gao, Chuishi Meng, Qi Li, Lu Su, finding. In IJCAI 2011, Proceedings of the 22nd Bo Zhao, Wei Fan, and Jiawei Han. 2016. A sur- International Joint Conference on Artificial Intel- vey on truth discovery. SIGKDD Explor. Newsl., ligence, Barcelona, Catalonia, Spain, July 16-22, 17(2):1–16. 2011, pages 2324–2329.

Yaliang Li, Qi Li, Jing Gao, Lu Su, Bo Zhao, Wei Fan, Jeff Pasternack and Dan Roth. 2013. Latent credibility and Jiawei Han. 2015. On the discovery of evolv- analysis. In Proceedings of the 22Nd International ing truth. In Proceedings of the 21th ACM SIGKDD Conference on World Wide Web, WWW ’13, pages International Conference on Knowledge Discovery 1009–1020, New York, NY, USA. ACM. and Data Mining, KDD ’15, pages 675–684, New York, NY, USA. ACM. Jeffrey Pennington, Richard Socher, and Christo- pher D. Manning. 2014. Glove: Global vectors for Chenghua Lin, Yulan He, and Richard Everson. word representation. In Empirical Methods in Natu- 2011. Sentence subjectivity detection with weakly- ral Language Processing, EMNLP ’14. supervised learning. In Proceedings of 5th Interna- tional Joint Conference on Natural Language Pro- Kashyap Popat, Subhabrata Mukherjee, Jannik cessing, pages 1153–1161. Asian Federation of Nat- Strotgen,¨ and Gerhard Weikum. 2016. Credibil- ural Language Processing. ity assessment of textual claims on the web. In Shanshan Lyu, Wentao Ouyang, Huawei Shen, and Proceedings of the 25th ACM International on Xueqi Cheng. 2017. Truth discovery by claim and Conference on Information and Knowledge Man- source embedding. In Proceedings of the 2017 ACM agement, CIKM ’16, pages 2173–2178, New York, on Conference on Information and Knowledge Man- NY, USA. ACM. agement, CIKM ’17, pages 2183–2186, New York, NY, USA. ACM. Kashyap Popat, Subhabrata Mukherjee, Jannik Strotgen,¨ and Gerhard Weikum. 2017. Where the Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing truth lies: Explaining the credibility of emerging Gao, Shi Zhi, Lu Su, Bo Zhao, Heng Ji, and Jiawei claims on the web and social media. In Proceedings Han. 2015. Faitcrowd: Fine grained truth discovery of the 26th International Conference on World Wide for crowdsourced data aggregation. In Proceedings Web Companion, WWW ’17 Companion. of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD Vahed Qazvinian, Emily Rosengren, Dragomir R. ’15, pages 745–754, New York, NY, USA. ACM. Radev, and Qiaozhu Mei. 2011. Rumor has it: Iden- tifying misinformation in microblogs. In Proceed- Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, ings of the Conference on Empirical Methods in Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Natural Language Processing, EMNLP ’11, pages Cha. 2016. Detecting rumors from microblogs with 1589–1599, Stroudsburg, PA, USA. Association for recurrent neural networks. In Proceedings of the Computational Linguistics. Twenty-Fifth International Joint Conference on Ar- tiﬁcial Intelligence, IJCAI’16, pages 3818–3824. Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana AAAI Press. Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and polit- Subhabrata Mukherjee and Gerhard Weikum. 2015. ical fact-checking. In Proceedings of the 2017 Con- Leveraging joint interactions for credibility analysis ference on Empirical Methods in Natural Language in news communities. In Proceedings of the 24th Processing, EMNLP ’17. ACM International on Conference on Information and Knowledge Management, CIKM ’15. Marta Recasens, Cristian Danescu-Niculescu-Mizil, and Dan Jurafsky. 2013. Linguistic models for an- Subhabrata Mukherjee, Gerhard Weikum, and Cristian alyzing and detecting biased language. In Proceed- Danescu-Niculescu-Mizil. 2014. People on drugs: ings of the 51st Annual Meeting of the Association

31 for Computational Linguistics (Volume 1: Long Pa- Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. 2012. pers), pages 1650–1659. Association for Computa- Automatic detection of rumor on sina weibo. In Pro- tional Linguistics. ceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS ’12, pages 13:1–13:7, New Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. York, NY, USA. ACM. Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Xiaoxin Yin, Jiawei Han, and Philip S. Yu. 2008. Information and Knowledge Management, CIKM Truth discovery with multiple conflicting informa- ’17, pages 797–806, New York, NY, USA. ACM. tion providers on the web. IEEE Trans. on Knowl. and Data Eng., 20(6):796–808. Mehdi Samadi, Partha Talukdar, Manuela Veloso, and Manuel Blum. 2016. Claimeval: Integrated and Zhe Zhao, Paul Resnick, and Qiaozhu Mei. 2015. En- flexible framework for claim evaluation using cred- quiring minds: Early detection of rumors in social ibility of sources. In Proceedings of the Thir- media from enquiry posts. In Proceedings of the tieth AAAI Conference on Artificial Intelligence, 24th International Conference on World Wide Web, AAAI’16, pages 222–228. AAAI Press. WWW ’15, pages 1395–1405, Republic and Canton of Geneva, Switzerland. International World Wide Vikram Singh, Sunny Narayan, Md. Shad Akhtar, Asif Web Conferences Steering Committee. Ekbal, and Pushpak Bhattacharyya. 2017. IITP at semeval-2017 task 8 : A supervised approach for Shi Zhi, Bo Zhao, Wenzhu Tong, Jing Gao, Dian Yu, rumour evaluation. In Proceedings of the 11th In- Heng Ji, and Jiawei Han. 2015. Modeling truth ex- ternational Workshop on Semantic Evaluation, Se- istence in truth discovery. In Proceedings of the 21th mEval@ACL 2017, Vancouver, Canada, August 3-4, ACM SIGKDD International Conference on Knowl- 2017, pages 497–501. edge Discovery and Data Mining, KDD ’15, pages 1543–1552, New York, NY, USA. ACM. Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. 2017. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In Proceedings of the 55th An- nual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 647– 653. Association for Computational Linguistics.

V.G. Vinod Vydiswaran, ChengXiang Zhai, and Dan Roth. 2011. Content-driven trust propagation framework. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11, pages 974–982, New York, NY, USA. ACM.

William Yang Wang. 2017. ”liar, liar pants on ﬁre”: A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the As- sociation for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 2: Short Papers, pages 422–426.

Janyce Wiebe and Ellen Riloff. 2005. Creating subjec- tive and objective sentence classiﬁers from unanno- tated texts. In Proceedings of the 6th International Conference on Computational Linguistics and Intel- ligent Text Processing, CICLing’05, pages 486–497, Berlin, Heidelberg. Springer-Verlag.

John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2015. Towards universal paraphrastic sentence embeddings. In Proceedings of the Inter- national Conference on Learning Representations (ICLR).

Qiongkai Xu and Hai Zhao. 2012. Using deep linguistic features for ﬁnding deceptive opinion spam. In Proceedings of COLING 2012: Posters, pages 1341–1350. The COLING 2012 Organizing Com- mittee.