Negative Link Prediction and Its Applications in Online Political Networks

Özer, Mert; Yildirim, Mehmet Yigit; Davulcu, Hasan

doi:10.1145/3078714.3078727

Cited by 13 publications

(9 citation statements)

References 24 publications

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“…Over the past few years, social bots have become increasingly prevalent on social media platforms, such that they now comprise approximately 9–15% of active Twitter accounts [ 20 ]. In the Twittersphere, social bots can drive high volumes of traffic, amplify polarizing and conspiracy content [ 52 , 53 ], and contribute to the circulation of misinformation [ 54 ].…”

Section: Literaturementioning

confidence: 99%

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Shi

Liu

Yang

et al. 2020

IJERPH

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During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

show abstract

Section: Literaturementioning

confidence: 99%

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Shi

Liu

Yang

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Another example of signed link prediction application is in political analysis that one needs to complete a picture of online political landscape. Inferring the signed links in political networks can further help description of rivalries and coalitions between groups [68].…”

Section: Social-theory Guided Feature Engineering For Signed Link Predictionmentioning

confidence: 99%

Social Science–guided Feature Engineering

Beigi

Tang

Liu

2020

ACM Trans. Intell. Syst. Technol.

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Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as recommendation systems. Most existing work on link analysis focuses on unsigned social networks. The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks and mandates dedicated efforts on link analysis for signed social networks. Recent findings suggest that properties of signed networks substantially differ from those of unsigned networks and negative links can be of significant help in signed link analysis in complementary ways. In this article, we center our discussion on a challenging problem of signed link analysis . Signed link analysis faces the problem of data sparsity, i.e., only a small percentage of signed links are given. This problem can even get worse when negative links are much sparser than positive ones as users are inclined more toward positive disposition rather than negative. We investigate how we can take advantage of other sources of information for signed link analysis. This research is mainly guided by three social science theories, Emotional Information , Diffusion of Innovations , and Individual Personality . Guided by these, we extract three categories of related features and leverage them for signed link analysis. Experiments show the significance of the features gleaned from social theories for signed link prediction and addressing the data sparsity challenge.

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“…On the technical side of this nurturing, a good example would be the 2012 experiment by Facebook to manipulate users' news feeds to determine whether emotionally positive or negative reactions could be invoked algorithmically [5]. Although such proactive manipulation of masses is beyond the reach of academic research, related negativity propagation topics have been examined also in marketing [2] and computer science [23]. Propagation provides a powerful tool also in politics.…”

Section: Hypothesesmentioning

confidence: 99%

A Dip Into a Deep Well: Online Political Advertisements, Valence, and European Electoral Campaigning

Ruohonen

2020

Preprint

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Online political advertisements have become an important element in electoral campaigning throughout the world. At the same time, concepts such as misinformation and manipulation have emerged as a global concern. Although these concepts are distinct from online political ads and data-driven electoral campaigning, they tend to share a similar trait related to valence, the intrinsic attractiveness or averseness of a message. Given this background, the paper examines online political ads by using a dataset collected from Google's transparency reports. The examination is framed to the mid-2019 situation in Europe, including the European Parliament election in particular. According to the results based on sentiment analysis of the textual ads displayed via Google's advertisement machinery, (i) most of the political ads have expressed positive sentiments, although these vary greatly between (ii) European countries as well as across (iii) European political parties. In addition to these results, the paper contributes to the timely discussion about datadriven electoral campaigning and its relation to politics and democracy.

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Negative Link Prediction and Its Applications in Online Political Networks

Cited by 13 publications

References 24 publications

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

Social Science–guided Feature Engineering

A Dip Into a Deep Well: Online Political Advertisements, Valence, and European Electoral Campaigning

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