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A Large News and Feature Data Set for the Study of the Complex Media Landscape

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A Large News and Feature Data Set for the Study of the Complex Media Landscape Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape Benjamin D. Horne, Sara Khedr, and Sibel Adalı Rensselaer Polytechnic Institute, Troy, New York, USA fhorneb, khedrs, [email protected] Abstract process and journalistically trained “gatekeepers”are insuf- ficient to counteract the potentially damaging effect these The complexity and diversity of today’s media landscape pro- sources have on the public (Mele et al. 2017) (Buntain vides many challenges for researchers studying news produc- and Golbeck 2017). As a result, there is a great deal of ers. These producers use many different strategies to get their message believed by readers through the writing styles they early research in automatically identifying different writ- employ, by repetition across different media sources with or ing styles and persuasion techniques employed by news without attribution, as well as other mechanisms that are yet sources (Popat et al. 2016) (Potthast et al. 2017) (Horne and to be studied deeply. To better facilitate systematic studies in Adalı 2017) (Chakraborty et al. 2016) (Singhania, Fernan- this area, we present a large political news data set, contain- dez, and Rao 2017). Hence, a broad data set including many ing over 136K news articles, from 92 news sources, collected different types of sources is especially useful in further re- over 7 months of 2017. These news sources are carefully cho- fining these methods. To this end, we include 130 content- sen to include well-established and mainstream sources, mali- based features for each article, in addition to the article meta- ciously fake sources, satire sources, and hyper-partisan politi- data and full text. The feature set contains almost all the fea- cal blogs. In addition to each article we compute 130 content- tures used in the related literature, such as identifying misin- based and social media engagement features drawn from a wide range of literature on political bias, persuasion, and mis- formation, political bias, clickbait, and satire. Furthermore, information. With the release of the data set, we also provide we include Facebook engagement statistics for each article the source code for feature computation. In this paper, we (the number of shares, comments, and reactions). discuss the first release of the data set and demonstrate 4 use While much of recent research has focused on automatic cases of the data and features: news characterization, engage- news characterization methods, there are many other news ment characterization, news attribution and content copying, publishing behaviors that are not well-studied. For instance, and discovering news narratives. there are many sources that have been in existence for a long time. These sources enjoy a certain level of trust by their Introduction audience, sometimes despite their biased and misleading re- porting, or potentially because of it. Hence, trust for sources The complexity and diversity of today’s media landscape and content cannot be studied independently. While misin- provides many challenges for researchers studying news. In formation in news has attracted a lot of interest lately, it is this paper, we introduce a broad news benchmark data set, important to note that many sources mix true and false in- called the NEws LAndscape (NELA2017) data set, to facili- formation in strategic ways to not only to distribute false in- tate the study of many problems in this domain. The data set formation, but also to create mistrust for other sources. This includes articles on U.S. politics from a wide range of news mistrust and uncertainty may be accomplished by writing sources that includes well-established news sources, satire specific narratives and having other similar sources copy that news sources, hyper-partisan sources (from both ends of the information verbatim (Lytvynenko 2017). In some cases, political spectrum), as well as, sources that have been known sources may copy information with the intention to misrep- to distribute maliciously fake information. At the time of resent it and undermine its reliability. In other cases, a source writing, this data set contains 136K news articles from 92 may copy information to gain credibility itself. Similarly, the sources between April 2017 and October 2017. coverage of topics in sources can be highly selective or may As news producers and distributors can be established include well-known conspiracy theories. Hence, it may be quickly with relatively little effort, there is limited prior data important to study a source’s output over time and compare on the reliability of some of many sources, even though the it to other sources publishing news in the same time frame. information they provide can end up being widely dissemi- This can sometimes be challenging as sources are known to nated due to algorithmic and social filtering in social media. remove articles that attract unwanted attention. We have ob- It has been argued that the traditionally slow fact-checking served this behavior with many highly shared false articles Copyright c 2018, Association for the Advancement of Artificial during the 2016 U.S. election. Intelligence (www.aaai.org). All rights reserved. These open research problems are the primary reasons we have created the NELA2017 data set. Instead of concentrat- sources. In addition, Kwak and An (Kwak and An 2016) ing on specific events or specific types of news, this data point out that there is concern as to how biased the GDELT set incorporates all political news production from a diverse data set is as it does not always align with other event group of of sources over time. While many news data sets based data sets. Unfiltered News (unfiltered.news) is have been published, none of them have the broad range of a service built by Google Ideas and Jigsaw to address filter sources and time frame that our data set offers. Our hope is bubbles in online social networks. Unfiltered News indexes that our data set can help serve as a starting point for many news data for each country based on mentioned topics. This exploratory news studies, and provide a better, shared insight data set does not focus on raw news articles or necessarily into misinformation tactics. Our aim is to continuously up- false news, but on location-based topics in news, making it date this data set, expand it with new sources and features, extremely useful for analyzing media attention across time as well as maintain completeness over time. and location. Data from Unfiltered News is analyzed in (An, In the rest of the paper, we describe the data set in de- Aldarbesti, and Kwak 2017). tail and provide a number of motivating use cases. The first There are many more data sets that focus on news or describe how we can characterize the news sources using claims in social networks. CREDBANK is a crowd sourced the features we have provided. In the second, we show how data set of 60 million tweets between October 2015 and social media engagement differs across groups sources. We February 2016. Each tweet is associated to a news event then illustrate content copying behavior among the sources and is labeled with credibility by Amazon Mechanical Turk- and how the sources covered different narratives around two ers (Mitra and Gilbert 2015). This data set does not contain events. raw news articles, only news article related tweets. PHEME is a data set similar to CREDBANK, containing tweets Related Work surrounding rumors. The tweets are annotated by journal- ist (Zubiaga et al. 2016). Once again, this data set does not There are several recent news data sets, specifically focused contain raw news articles, but focused on tweets spread- on fake news. These data sets include the following. ing news and rumors. Both PHEME and CREDBANK are Buzzfeed 2016 contains a sample of 1.6K fact-checked analyzed in (Buntain and Golbeck 2017). Hoaxy is an on- news articles from mainstream, fake, and political blogs line tool that visualizes the spread of claims and related shared on Facebook during the 2016 U.S. Presidential Elec- fact checking (Shao et al. 2016). Claim related data can be tion 1. It was later enhanced with meta data by Potthast et collected using the Hoaxy API. Once again, data from this al. (Potthast et al. 2017). This data set is useful for under- tool is focused on the spread of claims (which can be many standing the false news spread during the 2016 U.S. Presi- things: fake news article, hoaxes, rumors, etc.) rather than dential Election, but it is unknown how generalizable results news articles themselves. will be over different events. LIAR is a fake news bench- Other works use study-specific data sets collected from mark data set of 12.8K hand-labeled, fact-checked short a few sources. Some of these data sets are publicly avail- statements from politifact.com (Wang 2017). This able. Piotrkowicz et al. use 7 months of news data collected data set is much larger than many previous fake news data from The Guardian and The New York Times to assess sets, but focuses on short statements rather than complete headline structure’s impact on popularity (Piotrkowicz et al. news articles or sources. NECO 2017 contains a random 2017). Reis et al. analyze sentiment in 69K headlines col- sample of three types of news during 2016: fake, real, and lected from The New York Times, BBC, Reuters, and Dai- satire. Each source was hand-labeled using two online lists. lymail (Reis et al. 2015). Qian and Zhai collect news from It contains a total of 225 articles (Horne and Adalı 2017). CNN and Fox News to study unsupervised feature selection While the ground truth is reasonably based, the data set is on text and image data from news (Qian and Zhai 2014).
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