Estimating Ideological Scores of Facebook Pages: An Empirical Study in Taiwan

Wang, Ming-Hung; Chang, Alex Chuan-hsien; Chen, Kuan-Ta; Lei, Chin-Laung

doi:10.1093/comjnl/bxx045

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…A total of 19 pages which attract at least 25 thousand "like" activities from individual accounts during a 6-month-long observation are included in our list. Next, we refer to the ideological measurement methods proposed in the previous studies (Bond and Messing, 2015;Wang et al, 2017) and extract the interactions between unique accounts and media pages from the data collection. Next, we construct a co-emotion matrix 2 between each two fan pages.…”

Section: Methodsmentioning

confidence: 99%

“…Our methodology is motivated from the previous two studies (Bond and Messing, 2015;Wang et al, 2017). Before 2016, Facebook only built the "like" option for users to express their attitudes; thus, in the previous studies, "like" activity could combine various emotions, e.g., "like" and "loves."…”

Section: Retrieving Left Singular Vectors From Co-emotion Matrixmentioning

confidence: 99%

“…According to the study (Bond and Messing, 2015;Wang et al, 2017), the top LSV can explain the ideology of political celebrities. We retrieve the top LSV of R ′ i , u 1 u 2 .…”

Section: Retrieving Left Singular Vectors From Co-emotion Matrixmentioning

confidence: 99%

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Understanding Mass Media Using Facebook Like Activities

Wang¹

2019

Proceedings of the Conference for Truth and Trust Online 2019

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With the emergence of social network services, many media organizations have established their fan pages on social platforms (e.g., Facebook and Twitter). They promote their content, disseminate breaking news, interact with the audience, and publish editorials on particular events through fan pages. For readers, these online channels not only help them to receive the latest information but also enable them to directly express their endorsement or disagreement toward specific news or articles. According to the observation, in this study, we use the online behaviors to investigate favors/controversy from users to media organizations and to quantify the ideological difference of popular channels and their audience. We collect and analyze the actions of mass media pages on Facebook in a 6-month-long observation in 2018. A total of 19 fan pages of major mass media companies in Taiwan are investigated, and we locate their positions in a two-dimensional space using singular value decomposition. The results demonstrate that online activities from users can help to distinguish and cluster media companies based on several characteristics, including media types (magazines or newspapers), political stands, and their owners. These findings also correspond to previous offline qualitative studies in communication and media disciplines.

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Section: Methodsmentioning

confidence: 99%

Section: Retrieving Left Singular Vectors From Co-emotion Matrixmentioning

confidence: 99%

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Understanding Mass Media Using Facebook Like Activities

Wang¹

2019

Proceedings of the Conference for Truth and Trust Online 2019

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“…With the rapid development of social network services (SNS), the sources of political information have been extended to the SNS sites, such as Facebook, Twitter, and other platforms [24]- [27]. However, the increasing popularity of online political discussions also leads to frequent online disputes [28], [29].…”

Section: Election Cyber Armies Cyberbullying and Online Conflictsmentioning

confidence: 99%

Blurring Political Leanings of Messages on Social Networks Using Deniable Steganography

Wang

Chi

2019

IEEE Access

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While social network services have become popular platforms where large amounts of user-generated content are published, individuals, election campaigns, and political organizations leverage the services to publish, promote, and exchange political messages. However, sometimes, political discussions resulted in heated debates, doxing, and even cyberbullying. These conflicts may decrease the engagement of users and prevent them from publishing articles with apparent political orientation. To help users to post political opinion among the Internet freely, in this paper, we employ the concept of deniable encryption to generate neutral-like articles composed of two critics toward both sides of the discussion, while one of the critics is the actual message from the author to particular recipients. For receivers who know the credential information (e.g., key and codebook), they can communicate with each other by extracting the real information from the neutral-like articles. For people with the opposite stand, they cannot claim the message that is supporting specific political entity, as it contains disapproval to both stands; besides, they cannot prove that which part of the message is the plaintext even if they intercept the messages. We provide a generalized process of our proposal and present a practical scenario using transcripts, Facebook posts of Donald Trump, and posts of Hillary Clinton to demonstrate the usability of our scheme.

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Positioning and Categorizing Mass Media Using Reaction Emojis on Facebook

Wang

2020

The Computer Journal

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With the rapid growth of social network services, a paradigm shift in communication between media organizations and the audience has occurred. Numerous mass media agencies established fan pages on social platforms, such as Facebook, Twitter and Instagram, to disseminate breaking news, promote reports and interact with their audience. In this study, we leverage the reaction emojis delivered from users to media fan pages on Facebook to investigate how users react to media organizations and the implications of selective exposure. Using a 1-year-long observation of user activities on mass media pages, we perform a series of quantitative approaches to locate media agencies, measure the distances between them and cluster organizations into groups. A total of 30 fan pages of mass media organizations in Taiwan are investigated. The outcomes suggest that the report genres and topics are key factors to categorize media groups through reaction emojis from the online audience.

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Estimating Ideological Scores of Facebook Pages: An Empirical Study in Taiwan

Cited by 6 publications

References 13 publications

Understanding Mass Media Using Facebook Like Activities

Understanding Mass Media Using Facebook Like Activities

Blurring Political Leanings of Messages on Social Networks Using Deniable Steganography

Positioning and Categorizing Mass Media Using Reaction Emojis on Facebook

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