Predicting Swedish elections with Twitter : A case for stochastic link structure analysis

• The question that whether Twitter data can be leveraged to forecast outcome of the elections has always been of great anticipation in the research community. Existing research focuses on leveraging content analysis for positivity or negativity analysis of the sentiments of opinions expressed. This is while, analysis of link structure features of social networks underlying the conversation involving politicians has been less looked. The intuition behind such study comes from the fact that density of conversations about parties along with their respective members, whether explicit or implicit, should reflect on their popularity. On the other hand, dynamism of interactions, can capture the inherent shift in popularity of accounts of politicians. Within this manuscript we present evidence of how a well-known link prediction algorithm, can reveal an authoritative structural link formation within which the popularity of the political accounts along with their neighbourhoods, shows strong correlation with the standing of electoral outcomes. As an evidence, the public time-lines of two electoral events from 2014 elections of Sweden on Twitter have been studied. By distinguishing between member and official party accounts, we report that even using a focus-crawled public dataset, structural link popularities bear strong statistical similarities with vote outcomes. In addition we report strong ranked dependence between standings of selected politicians and general election outcome, as well as for official party accounts and European election outcome.

Subject headings

SAMHÄLLSVETENSKAP  -- Statsvetenskap -- Statsvetenskap (hsv//swe)

SOCIAL SCIENCES  -- Political Science -- Political Science (hsv//eng)

Keyword

Algorithms

Social networking (online)

Stochastic systems

Content analysis

General Elections

Link prediction

Link structure analysis

Public dataset

Research communities

Strong correlation

Structural links

Forecasting

Publication and Content Type

ref (subject category)

kon (subject category)