Can we predict electoral outcomes from Wikipedia traffic?

by taha yasseri 06/12/2016

Data show that the relative change in page views to the general Wikipedia page on the election can offer an estimate of the relative change in election turnout.

by taha yasseri 06/12/2016

2016 presidential candidate Donald Trump in a residential backyard near Jordan Creek Parkway and Cody Drive in West Des Moines, Iowa, with lights and security cameras. Image by Tony Webster (Flickr).

As digital technologies become increasingly integrated into the fabric of social life their ability to generate large amounts of information about the opinions and activities of the population increases. The opportunities in this area are enormous: predictions based on socially generated data are much cheaper than conventional opinion polling, offer the potential to avoid classic biases inherent in asking people to report their opinions and behaviour, and can deliver results much quicker and be updated more rapidly. In their article published in EPJ Data Science, Taha Yasseri and Jonathan Bright develop a theoretically informed prediction of election results from socially generated data combined with an understanding of the social processes through which the data are generated. They can thereby explore the predictive power of socially generated data while enhancing theory about the relationship between socially generated data and real world outcomes. Their particular focus is on the readership statistics of politically relevant Wikipedia articles (such as those of individual political parties) in the time period just before an election. By applying these methods to a variety of different European countries in the context of the 2009 and 2014 European Parliament elections they firstly show that the relative change in number of page views to the general Wikipedia page on the election can offer a reasonable estimate of the relative change in election turnout at the country level. This supports the idea that increases in online information seeking at election time are driven by voters who are considering voting. Second, they show that a theoretically informed model based on previous national results, Wikipedia page views, news media mentions, and basic information about the political party in question can offer a good prediction of the overall vote share of the party in question. Third, they present a model for predicting change in vote share (i.e., voters swinging towards and away from a party), showing that Wikipedia page-view data provide an important increase…

Social Data Science

Edit wars! Examining networks of negative social interaction

by taha yasseri 04/11/2016

While these interactions are less common, they strongly affect people’s psychological well-being, physical health, and work performance.

by taha yasseri 04/11/2016

Network of all reverts done in the English language Wikipedia within one day (January 15, 2010). Read the full article for details

While network science has significantly advanced our understanding of the structure and dynamics of the human social fabric, much of the research has focused on positive relations and interactions such as friendship and collaboration. Considerably less is known about networks of negative social interactions such as distrust, disapproval, and disagreement. While these interactions are less common, they strongly affect people’s psychological well-being, physical health, and work performance. Negative interactions are also rarely explicitly declared and recorded, making them hard for scientists to study. In their new article on the structural and temporal features of negative interactions in the community, Milena Tsvetkova, Ruth García-Gavilanes and Taha Yasseri use complex network methods to analyse patterns in the timing and configuration of reverts of article edits to Wikipedia. In large online collaboration communities like Wikipedia, users sometimes undo or downrate contributions made by other users; most often to maintain and improve the collaborative project. However, it is also possible that these actions are social in nature, with previous research acknowledging that they could also imply negative social interactions. The authors find evidence that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. However, they don’t find evidence that editors “pay forward” a revert, coordinate with others to revert an editor, or revert different editors serially. These interactions can be related to the status of the editors. Even though the individual reverts might not necessarily be negative social interactions, their analysis points to the existence of certain patterns of negative social dynamics within the editorial community. Some of these patterns have not been previously explored and certainly carry implications for Wikipedia’s own knowledge collection practices—and can also be applied to other large-scale collaboration networks to identify the existence of negative social interactions. Read the full article: Milena Tsvetkova, Ruth García-Gavilanes and Taha Yasseri (2016) Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in…

social data science