Abstract
In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this paper we present a novel rumour detection system which focuses on a specific topic, that is health-related rumours on Twitter. To this aim, we constructed a new subset of features including influence potential and network characteristics features. We tested our approach on a real dataset observing promising results, as it is able to correctly detect about 89% of rumours, with acceptable levels of precision.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 1599-1606 |
| Number of pages | 8 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781509030491 |
| DOIs | |
| Publication status | Published - 15 Dec 2017 |
| Event | 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017) - Kansas City, United States Duration: 13 Nov 2017 → 16 Nov 2017 |
Conference
| Conference | 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017) |
|---|---|
| Abbreviated title | BIBM 2017 |
| Country | United States |
| City | Kansas City |
| Period | 13/11/17 → 16/11/17 |
Keywords
- Health rumour detection
- Network- and User-based Features
- Social microblog