Details
| Original language | English |
|---|---|
| Title of host publication | WebSci 2018 - Proceedings of the 10th ACM Conference on Web Science |
| Pages | 221-228 |
| Number of pages | 8 |
| ISBN (electronic) | 9781450355636 |
| Publication status | Published - 15 May 2018 |
| Event | 10th ACM Conference on Web Science, WebSci 2018 - Amsterdam, Netherlands Duration: 27 May 2018 → 30 May 2018 |
Abstract
In this paper we propose a semi-supervised learning strategy to automatically separate fake News from reliable News sources: DistrustRank. We first select a small set of unreliable News, manually evaluated and classified by experts on fact checking portals. Once this set is created, DistrustRank constructs a weighted graph where nodes represent websites, connected by edges based on a minimum similarity between a pair of websites. Next it computes the centrality using a biased PageRank, where a bias is applied to the selected set of seeds. As an output of the proposed model we obtain a trust (or distrust) rank that can be used in two ways: a) as a counter-bias to be applied when News about a specific subject is ranked, in order to discount possible boosts achieved by false claims; and b) to assist humans to identify sources that are likely to be source of fake News (or that are likely to be reputable), suggesting websites that should be examined more closely or to be avoided. In our experiments, DistrustRank outperforms the supervised approaches in either ranking and classification task.
Keywords
- Credibility analysis, Rumor detection, Text mining
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
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WebSci 2018 - Proceedings of the 10th ACM Conference on Web Science. 2018. p. 221-228.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - DistrustRank
T2 - 10th ACM Conference on Web Science, WebSci 2018
AU - Woloszyn, Vinicius
AU - Nejdl, Wolfgang
N1 - Funding information: This work was partially funded by the European Research Council under ALEXANDRIA (ERC 339233) and CAPES, a Brazilian government institution for scientific development.
PY - 2018/5/15
Y1 - 2018/5/15
N2 - In this paper we propose a semi-supervised learning strategy to automatically separate fake News from reliable News sources: DistrustRank. We first select a small set of unreliable News, manually evaluated and classified by experts on fact checking portals. Once this set is created, DistrustRank constructs a weighted graph where nodes represent websites, connected by edges based on a minimum similarity between a pair of websites. Next it computes the centrality using a biased PageRank, where a bias is applied to the selected set of seeds. As an output of the proposed model we obtain a trust (or distrust) rank that can be used in two ways: a) as a counter-bias to be applied when News about a specific subject is ranked, in order to discount possible boosts achieved by false claims; and b) to assist humans to identify sources that are likely to be source of fake News (or that are likely to be reputable), suggesting websites that should be examined more closely or to be avoided. In our experiments, DistrustRank outperforms the supervised approaches in either ranking and classification task.
AB - In this paper we propose a semi-supervised learning strategy to automatically separate fake News from reliable News sources: DistrustRank. We first select a small set of unreliable News, manually evaluated and classified by experts on fact checking portals. Once this set is created, DistrustRank constructs a weighted graph where nodes represent websites, connected by edges based on a minimum similarity between a pair of websites. Next it computes the centrality using a biased PageRank, where a bias is applied to the selected set of seeds. As an output of the proposed model we obtain a trust (or distrust) rank that can be used in two ways: a) as a counter-bias to be applied when News about a specific subject is ranked, in order to discount possible boosts achieved by false claims; and b) to assist humans to identify sources that are likely to be source of fake News (or that are likely to be reputable), suggesting websites that should be examined more closely or to be avoided. In our experiments, DistrustRank outperforms the supervised approaches in either ranking and classification task.
KW - Credibility analysis
KW - Rumor detection
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85049402001&partnerID=8YFLogxK
U2 - 10.1145/3201064.3201083
DO - 10.1145/3201064.3201083
M3 - Conference contribution
AN - SCOPUS:85049402001
SP - 221
EP - 228
BT - WebSci 2018 - Proceedings of the 10th ACM Conference on Web Science
Y2 - 27 May 2018 through 30 May 2018
ER -