Lampos, V;
Aletras, N;
Preoţiuc-Pietro, D;
Cohn, T;
(2014)
Predicting and characterising user impact on Twitter.
In:
(pp. pp. 405-413).
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Abstract
The open structure of online social networks and their uncurated nature give rise to problems of user credibility and influence. In this paper, we address the task of predicting the impact of Twitter users based only on features under their direct control, such as usage statistics and the text posted in their tweets.We approach the problem as regression and apply linear as well as non-linear learning methods to predict a user impact score, estimated by combining the numbers of the user's followers, followees and listings. The experimental results point out that a strong prediction performance is achieved, especially for models based on the Gaussian Processes framework. Hence, we can interpret various modelling components, transforming them into indirect 'suggestions' for impact boosting. © 2014 Association for Computational Linguistics.
| Type: | Proceedings paper |
|---|---|
| Title: | Predicting and characterising user impact on Twitter |
| ISBN-13: | 9781632663962 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3115/v1/e14-1043 |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1417763 |
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