Information systems such as social media strongly influence public opinion formation. Additionally, communication on the internet is shaped by individuals and organisations with various aims. This environment has given rise to phenomena such as manipulated content, fake news, and social bots. To examine the influence of manipulated opinions, we draw on the spiral of silence theory and complex adaptive systems. We translate empirical evidence of individual behaviour into an agent-based model and show that the model results in the emergence of a consensus on the collective level. In contrast to most previous approaches, this model explicitly represents interactions as a network. The most central actor in the network determines the final consensus 60-70% of the time. We then use the model to examine the influence of manipulative actors such as social bots on public opinion formation. The results indicate that, in a highly polarised setting, depending on their network position and the overall network density, bot participation by as little as 2-4% of a communication network can be sufficient to tip over the opinion climate in two out of three cases. These findings demonstrate a mechanism by which bots could shape the norms adopted by social media users.

Wikipedia is an important source of information in today's world. Yet, the lack of gender diversity in its community has been shown to affect the topics covered. Each Wikipedia article has a talk page that volunteer editors use to discuss proposed changes. Research on the gender bias has focused on article contribution and topic coverage, but not talk page activity. It has been suggested that the conflicts that take place in talk pages are especially intimidating for women, but this assertion has not been quantified yet. To fill this gap, we collected a dataset of all comments on Wikipedia talk pages, enriching it with gender information available from users who have chosen to disclose their gender on their user profiles or settings. Among the users active in talk pages, 49,387 indicated that they are male while only 5,996 indicated that they are female. The comments of these users make up for 4 million comments, approximately one quarter of all comments on Wikipedia. In addition, we observed that female participation varies by topic, reflecting traditional gender stereotypes: compared to Science, Technology, Engineering and Mathematics (STEM) topics, women were more active in categories such as Gender studies or Feminism. Results also indicate that a post on a talk page is 2.4\% less likely to be replied to if the author is female. Likewise, reply probability varies from topic to topic. These results provide quantitative support for a gender bias in Wikipedia talk pages, and serve as a basis for discussing why overall female participation is low.

Social media has become important in shaping the public discourse on controversial topics. Many businesses therefore monitor different social media channels and try to react adequately to a potentially harmful opinion climate. Still, little is known about how opinions form in an increasingly connected world. The spiral of silence theory provides a way of explaining deviations between the perceived opinion climate and true beliefs of the public. However, the emergence of a spiral of silence on social media is hard to observe because only the thoughts of those who express their opinions are evident there. Recent research has therefore focused on modelling the processes behind the spiral of silence. A particular characteristic of social media networks is the presence of communities. Members of a community tend to be connected more with other members of the same community than with outsiders. Naturally, this might affect the development of public opinion. In the present article we investigate how the number of communities in a network and connectivity between them affects the perceived opinion climate. We find that higher connectivity between communities makes it more likely for a global spiral of silence to appear. Moreover, a network fragmented into more, smaller communities seems to provide more “safe spaces” for a minority opinion to prevail.

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Wikipedia offers researchers unique insights into the collaboration and communication patterns of a large self-regulating community of editors. The main medium of direct communication between editors of an article is the article's talk page. However, a talk page file is unstructured and therefore difficult to analyse automatically. A few parsers exist that enable its transformation into a structured data format. However, they are rarely open source, support only a limited subset of the talk page syntax -resulting in the loss of content -and usually support only one export format. Together with this article we offer a very fast, lightweight, open source parser with support for various output formats. In a preliminary evaluation it achieved a high accuracy. The parser uses a grammar-based approach -offering a transparent implementation and easy extensibility.

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