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Predicting Swedish Elections with Twitter: A Case for Stochastic Link Structure Analysis
KTH.
KTH, Skolan för informations- och kommunikationsteknik (ICT), Programvaruteknik och Datorsystem, SCS.ORCID-id: 0000-0002-4722-0823
2015 (Engelska)Ingår i: PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), Association for Computing Machinery (ACM), 2015, s. 1269-1276Konferensbidrag, Publicerat paper (Refereegranskat)
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Text
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM), 2015. s. 1269-1276
Nationell ämneskategori
Statsvetenskap (exklusive studier av offentlig förvaltning och globaliseringsstudier)
Identifikatorer
URN: urn:nbn:se:kth:diva-185414DOI: 10.1145/2808797.2808915ISI: 000371793500194Scopus ID: 2-s2.0-84962601806ISBN: 978-1-4503-3854-7 (tryckt)OAI: oai:DiVA.org:kth-185414DiVA, id: diva2:920284
Konferens
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), AUG 25-28, 2015, Paris, FRANCE
Projekt
Networks
Anmärkning

QC 20160418

Tillgänglig från: 2016-04-18 Skapad: 2016-04-18 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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Matskin, Mihhail

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Zikou, FilippiaMatskin, Mihhail
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Statsvetenskap (exklusive studier av offentlig förvaltning och globaliseringsstudier)

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