Learning to Discover Political Activism in the Twitterverse
| dc.contributor.author | Finn, Samantha | |
| dc.contributor.author | Mustafaraj, Eni | |
| dc.date.accessioned | 2018-01-08T09:16:22Z | |
| dc.date.available | 2018-01-08T09:16:22Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | When analysing social media conversations, in search of the public opinion about an unfolding political event that is being discussed in real-time (e.g., presidential debates, major speeches, etc.), it is important to distinguish between two groups of participants: political activists and the general public. To address this problem, we propose a supervised machine-learning approach, which uses inexpensively acquired labeled data from mono-thematic Twitter accounts to learn a binary classifier for the labels “political activist” and “general public”. While the classifier has a 92 % accuracy on individual tweets, when applied to the last 200 tweets from accounts of a set of 1000 Twitter users, it classifies accounts with a 97 % accuracy. Our work demonstrates that machine learning algorithms can play a critical role in improving the quality of social media analytics and understanding, whose importance is increasing as social media adoption becomes widespread. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11332 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 27, No. 1 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Machine learning | |
| dc.subject | Political discourse | |
| dc.subject | Social media | |
| dc.subject | ||
| dc.title | Learning to Discover Political Activism in the Twitterverse | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 24 | |
| gi.citation.startPage | 17 |