Correlation analysis of reader's demographics and tweet credibility perception

Mohd Shariff, S, Sanderson, M and Zhang, X 2016, 'Correlation analysis of reader's demographics and tweet credibility perception', in Nicola Ferro, Fabio Crestani, Marie-Francine Moens, Josiane Mothe, Fabrizio Silvestri, Giorgio Maria Di Nunzio, Claudia Hauff and Gianmaria Silvello (ed.) Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23 2016, pp. 453-465.


Document type: Conference Paper
Collection: Conference Papers

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Title Correlation analysis of reader's demographics and tweet credibility perception
Author(s) Mohd Shariff, S
Sanderson, M
Zhang, X
Year 2016
Conference name 38th European Conference on IR Research, ECIR 2016
Conference location Padua, Italy
Conference dates March 20-23 2016
Proceedings title Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016
Editor(s) Nicola Ferro
Fabio Crestani
Marie-Francine Moens
Josiane Mothe
Fabrizio Silvestri
Giorgio Maria Di Nunzio
Claudia Hauff
Gianmaria Silvello
Publisher Springer
Place of publication Cham, Switzerland
Start page 453
End page 465
Total pages 13
Abstract When searching on Twitter, readers have to determine the credibility level of tweets on their own. Previous work has mostly studied how the text content of tweets in uences credibility perception. In this paper, we study reader demographics and information credibility perception on Twitter. We nd reader's educational background and geolocation have signi cant correlation with credibility perception. Further investigation reveals that combinations of demographic attributes correlating with credibility perception are insigni cant. Despite di erences in demographics, readers nd features regarding topic keyword and the writing style of a tweet to be independently helpful in perceiving tweets' credibility. While previous studies reported the use of features independently, our result shows that readers use combination of features to help in making credibility perception of tweets.
Subjects Information Systems not elsewhere classified
DOI - identifier 10.1007/978-3-319-30671-1_33
Copyright notice © Springer International Publishing Switzerland 2016
ISBN 9783319306711
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