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A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities

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A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities Yuping Wang1, Savvas Zannettou2, Jeremy Blackburn3, Barry Bradlyn4, Emiliano De Cristofaro5, and Gianluca Stringhini1 1Boston University, 2Max Planck Institute for Informatics, 3Binghamton University, 4University of Illinois at Urbana-Champaign, 5University College London – iDRAMA Lab, https://idrama.science – Abstract ence each other in spreading single news URLs [96]. Overall, despite the crucial role played by online news in our society, The news ecosystem has become increasingly complex, en- we still lack computational tools to monitor how news stories compassing a wide range of sources with varying levels of unfold and are discussed across multiple online services. trustworthiness, and with public commentary giving different In this paper, we present a longitudinal study of how news is spins to the same stories. In this paper, we present a multi- disseminated across multiple Web communities. We introduce platform measurement of this ecosystem. We compile a list an analysis pipeline (which is independent of the data sources of 1,073 news websites and extract posts from four Web com- and thus reusable), consisting of different components to: 1) munities (Twitter, Reddit, 4chan, and Gab) that contain URLs collect data, 2) extract named entities, 3) group articles be- from these sources. This yields a dataset of 38M posts con- longing to the same story, and 4) estimate influence of a Web taining 15M news URLs, spanning almost three years. community on other ones. We study the data along several axes, assessing the trust- worthiness of shared news, designing a method to group news We instantiate the pipeline by focusing on a mix of main- articles into stories, analyzing these stories are discussed, stream and fringe communities – namely, Twitter, Reddit, and measuring the influence various Web communities have 4chan’s Politically Incorrect board (/pol/), and Gab – and ex- in that. Our analysis shows that different communities dis- tract 38M posts including 15M news URLs, spanning a pe- cuss different types of news, with polarized communities like riod of almost three years. We use named entity extraction Gab and /r/The_Donald subreddit disproportionately referenc- to analyze what types of news these communities discuss. We ing untrustworthy sources. We also find that fringe commu- also study the interplay between news discussion and the trust- nities often have a disproportionate influence on other plat- worthiness of the news sources cited using NewsGuard [58], a forms w.r.t. pushing narratives around certain news, for exam- trustworthiness assessment site compiled by professional fact ple about political elections, immigration, or foreign policy. checkers. Next, we perform community detection to group together related articles into news stories, and study how they are dis- 1 Introduction cussed on different Web communities. To this end, we use The Web has facilitated the growth of fast-paced, online-first GDELT, a dataset that labels global news events [42]. Because news sources. It has also allowed users to actively contribute of GDELT’s focus on politics, our measurement also concen- arXiv:2103.03631v1 [cs.CY] 5 Mar 2021 to and shape the discussion around news. This creates an en- trates on political news stories. Finally, to study the influence vironment where journalists are not necessarily the arbiters of that different Web communities have on each other in spread- how a news story develops and spreads. In today’s “hybrid” ing news stories, we use Hawkes Processes [31]. These allow media system [15], the popularity of a news story is also influ- us to estimate which news stories are organically discussed on enced by how users discuss it. Although such discussions usu- the Web, and for which ones certain communities exercise a ally happen organically, various actors from polarized online significant influence in spreading them. communities or state-sponsored troll farms might also attempt Our analysis yields the following main findings: to manipulate them, e.g., by pushing [20] or weaponizing [96] • When discussing news, different Web communities post certain narratives. URLs to news outlets with varying levels of trustworthi- Previous work studying the news ecosystem on the Web ness. In particular, Gab and /r/The_Donald (subreddit) has mostly focused on Twitter, looking at single news arti- prefer untrustworthy ones compared to Reddit, Twitter, cles [65, 73, 101], or on the discussion surrounding a small and 4chan. set of events [18, 79, 92]. Moreover, efforts to study the inter- • Some communities are particularly influential in the dis- twined relationship between news coverage and social media semination of news. While large ones like Twitter and discussions have been limited to direct quotes from news arti- Reddit have a consistent influence on the others, rela- cles posted on Twitter [44], or on how Web communities influ- tively small/fringe communities like /r/The_Donald have 1 Trustworthiness Assessment Sites Web Communities posting News News Sources Generic Trustworthiness Assessment Site Generic ... Generic ... Web News ... Communities Sources 4chan Twitter Reddit Gab Posts from Web communities News Sources and Trustworthiness Scores News Articles list of Data Collection of News Sources list of Extraction of all URLs using the Data collection of all news articles News and Trustworthiness Scores News Sources posts and list of news sources posted on the Web communities Sources URLs Data Collection Component Events identification for URLs TrueCase text via GDELT Timeseries compilation (Stanford CoreNLP) Graph Representation of URLs Named Entity Extraction Hawkes Processes Modelling (SpaCy) Community detection for news stories identification Natural Language Processing Component News Stories Identification Component Influence Estimation Component Figure 1: High-level overview of our processing pipeline. a much larger external influence, affecting the posting of varies over time [72], and low-reputation sites are often stories at a rate that is much larger than their relative size ephemeral. For instance, out of the 54 “alternative” news sites would suggest. studied in [96], only 23 are still active as of October 2020. • Some topics are very popular across the board, however, Thus, we opt to take a more systematic approach. First, we different communities focus on different narratives about gather popular domains using the top 30K websites from the the same story. For instance, /r/The_Donald and /pol/ Majestic list [48] as of February 2019. Then, we use the Virus- are particularly influential in spreading anti-immigration Total API [1], a service that provides domain categorization, rhetoric and conspiracy theories. to select only the domains categorized as news and media or news. Note that VirusTotal’s categorization is not exempt from mistakes; e.g., domains like ananova.com, adbusters.org, and 2 Methodology & Datasets cagle.com are misclassified as news sites. To further refine this In this section, we present the methodology used to analyze list, we use the NewsGuard API [58], a service that ranks news the appearance and discussion of news articles across multiple sources based on their trustworthiness, and restrict our analy- Web communities, as well as the datasets we collect in the sis to news sites that are rated by NewsGuard as of February process. Figure1 presents a high-level overview of our 2019, i.e., before it became a paid service. As a result, we pipeline, which consists of four components: obtain a total of 1,073 news websites. Source Trustworthiness. We use NewsGuard [58] also to 1. Data Collection: selecting news sources, collecting re- characterize the trustworthiness of a news Website, as it pro- lated posts from social media, and gathering the news vides credibility/transparency scores. The scores are based content. on nine journalistic criteria, focused on different aspects (e.g., 2. Natural Language Processing: identifying the “entities” whether a news site consistently publishes false content, uses mentioned in news articles and discussions in Web com- deceptive headlines, etc.) that do not take into account politi- munities. cal leanings, and range from 0 to 100. If the score is no less 3. News Stories Identification: grouping articles belonging than 60, the news source is labeled as trustworthy, and untrust- to the same story. worthy otherwise [56]. NewsGuard’s evaluation is conducted 4. Influence Estimation: assessing the influence of each Web by a team of experts [56], and this manual vetting provides us community on others with respect to news stories. with reasonable confidence in its accuracy. The threshold of 60 is pre-defined by NewsGuard, and its assessment evaluation is 2.1 Data Collection designed to fix the threshold first and then assign the points for This component is used to select a set of suitable news sources, each criterion according to this threshold [56]. Therefore, we determine their trustworthiness, and retrieve posts on different stick to this threshold. social networks including URLs to these sources. While our Overall, we are confident in the meaningfulness of News- methodology is general and can be used with any data source, Guard scores, as a recent Gallup study [23] has confirmed it in the following, we describe it along with the specific services as a useful tool to help readers identify untrustworthy news selected for this work. outlets. Approximately 600 news outlets in the US and News Sources.
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