News Outlet Tweet Ids

Littman, Justin; Wrubel, Laura; Kerchner, Daniel; Gaber, Yonah Bromberg

doi:10.7910/dvn/2fiflh

Cited by 3 publications

(1 citation statement)

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“…The Media Thesaurus is a news media list that contains the news domain and its news type classification. It is compiled from multiple publicly available lists: (1) from Media Bias/Fact Check 4 which lists many news sites and rates the factuality and credibility of the reporting; (2) the George Washington University Dataverse (Littman et al, 2020) which categorizes a list of over 9,600 Twitter accounts for media organizations that are derived from over 160 million Tweets between 2016 and 2020; (3) the Columbia Journalism Review as a source for hundreds of Pink Slime News outlet domains (Tow, 2020); (4) a Github Repository 5 that collages unreliable and misleading news sources from Snopes Field Guide, Wikipedia, and other domains; (5) a Github Repository 6 that consolidates a list of most frequented web domains and most frequently tweeted domains by U.S. politicians and the corresponding news type labels; and (6) a consolidation of Local News through a list of authentic local news sites owned by companies (Clemm von Hohenberg et al, 2021;Free Press, 2022). After consolidation, this Media Thesaurus is harmonized among the sources.…”

Section: Annotating News Labelsmentioning

confidence: 99%

What News Is Shared Where and How: A Multi-Platform Analysis of News Shared During the 2022 U.S. Midterm Elections

Lepird,

Ng,

et al. 2024

Social Media + Society

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News journalism has evolved from traditional print media to social media, with a large proportion of readers consuming their news via digital means. Through an analysis of over 1.3 million posts across three social media platforms (Facebook, Twitter, Reddit) pertaining to the 2022 U.S. Midterm Elections, this analysis examines the difference in sharing patterns for four types of news sites—Real News, Local News, Low Credibility News, and Pink Slime. Through Platform-Based Analysis, this study observes that users across all platforms share Real and Local News sequentially, and Real News and Low Credibility News sequentially. Through News Type-Based Analysis, this study establishes a Relative Engagement metric, demonstrating a widely varied engagement among the news types. Real News receive the least engagement (defined as the ratio of number of likes a post has vs. the number of followers of the page), while users engage with Pink Slime news the most. Furthermore, this study finds that the sharing of automated local news reporting sites (Pink Slime sites) are divided on political lines. Finally, through a User-Based Analysis, this study finds that automated bot users share a larger proportion of Pink Slime and Low Credibility News, while human users generally share content relating to local communities.

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Section: Annotating News Labelsmentioning

confidence: 99%

What News Is Shared Where and How: A Multi-Platform Analysis of News Shared During the 2022 U.S. Midterm Elections

Lepird,

Ng,

et al. 2024

Social Media + Society

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Visual analysis of the opinion flow among multiple social groups

Zhang

Robert

et al. 2019

J Vis

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User Representation Learning for Social Networks: An Empirical Study

Hallac

Aydın

2021

Applied Sciences

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Gathering useful insights from social media data has gained great interest over the recent years. User representation can be a key task in mining publicly available user-generated rich content offered by the social media platforms. The way to automatically create meaningful observations about users of a social network is to obtain real-valued vectors for the users with user embedding representation learning models. In this study, we presented one of the most comprehensive studies in the literature in terms of learning high-quality social media user representations by leveraging state-of-the-art text representation approaches. We proposed a novel doc2vec-based representation method, which can encode both textual and non-textual information of a social media user into a low dimensional vector. In addition, various experiments were performed for investigating the performance of text representation techniques and concepts including word2vec, doc2vec, Glove, NumberBatch, FastText, BERT, ELMO, and TF-IDF. We also shared a new social media dataset comprising data from 500 manually selected Twitter users of five predefined groups. The dataset contains different activity data such as comment, retweet, like, location, as well as the actual tweets composed by the users.

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News Outlet Tweet Ids

Cited by 3 publications

References 0 publications

What News Is Shared Where and How: A Multi-Platform Analysis of News Shared During the 2022 U.S. Midterm Elections

What News Is Shared Where and How: A Multi-Platform Analysis of News Shared During the 2022 U.S. Midterm Elections

Visual analysis of the opinion flow among multiple social groups

User Representation Learning for Social Networks: An Empirical Study

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