Gender Effects in Online Low-Threshold Evaluations: Evidence from a Large-Scale Online Discussion-based Community

Online communities thrive on the basis of interactions between like-minded individuals, and usually involve some form of feedback or evaluations by peers. In these contexts, there is systematic evidence of gender-based biases in evaluations. How can such biases be attenuated? We study the efficacy of one approach—anonymization of gender information on the community. We use data from a large-scale digital discussion platform, Political Science Rumors, to examine the presence of gender bias. When users on the community post a discussion message, they are randomly assigned a pseudonym in the form of a given (or first name), such as “Daniel” or “Haylee,” and each post subsequently garners positive and negative votes from readers. We analyze the up votes, down votes, and net votes garnered by 1.4 million posts where names are randomly assigned to posters. We find that posts from randomly assigned “female” names receive 2.5% lower evaluation scores, all else equal. Further, when “female” users post emotive content with a negative tone, the posts receive disproportionately more negative evaluations

Identifying and Quantifying Coordinated Manipulation of Upvotes and Downvotes in Naver News Comments

Today, many news sites let users write comments on news articles, rate others' comments by upvoting and downvoting, and order the comments by the rating. Top-rated comments are placed right below the news article and read widely, reaching a large audience and wielding great influence. As their importance grew, upvotes and downvotes are increasingly manipulated by coordinated efforts to hide existing top comments and push certain comments to the top. In this paper, we analyze comment sections of articles targeted by coordinated efforts and identify a trace of vote manipulation. Based on the findings, we propose a parameterized classifier that distinguishes comment threads affected by coordinated voting. The classifier only uses the number of upvotes and downvotes of comments. Therefore it is widely applicable to general vote-based curation systems where contents are sorted by the difference of upvotes and downvotes. Using the classifier and our choice of parameters, we have examined six years of the entire commenting history on a leading news portal in South Korea. Manual inspection in partisan online communities could only identify a few hundreds of targeted articles. With our classifier, we have identified more than ten thousand comment threads with a high likelihood of manipulation. We also observe a significant increase in coordinated manipulation in recent years

A network science framework for detecting disruptive behaviour on social media

Social networking platforms enable individuals to interact with others in a public forum by creating and/or consuming both written and visual content. Due to the popularity and wide-spread adoption of social media, this has led to unforeseen negative consequences where actors use social media to intentionally disrupt normal discourse to subversively influence individuals or groups. As a result, this leads to the problem of detecting anomalous activity, which is challenging due to large quantities of textual information combined with multimedia. Furthermore, this is compounded by issues such as foreign languages. This motivates research into techniques that can detect anomalies in social media activity through language-agnostic approaches. This thesis examines ways in which this can be achieved through network science, using different forms of networks to represent the behaviour of actors in social media, rather than the specific content they have produced. However, diverse affordances on alternative social media platforms make this a complex problem. This thesis examines three alternative classes of network representation with respect to detecting disruption in social media. We examine these representations using techniques from complex network theory. Using a range of social media systems, this thesis provides evidence that network based signals aligning to disruptive behaviours can be detected for alternative forms of social media engagement (e.g., collaboration, message, community and feed-based interactions). Through this approach, this thesis determines prospects for assessing social media in complex and dynamic scenarios without recourse to processing natural language