Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Social Informatics |
| Untertitel | 9th International Conference, SocInfo 2017, Proceedings |
| Herausgeber/-innen | Giovanni Luca Ciampaglia, Taha Yasseri, Afra Mashhadi |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 141-158 |
| Seitenumfang | 18 |
| ISBN (elektronisch) | 978-3-319-67256-4 |
| ISBN (Print) | 9783319672557 |
| Publikationsstatus | Veröffentlicht - 2 Sept. 2017 |
| Veranstaltung | 9th International Conference on Social Informatics, SocInfo 2017 - Oxford, Großbritannien / Vereinigtes Königreich Dauer: 13 Sept. 2017 → 15 Sept. 2017 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 10540 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Recently a lot of progress has been made in rumor modeling and rumor detection for micro-blogging streams. However, existing automated methods do not perform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the early phase of a rumor. In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights on the credibility of each tweets. We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model. Our extensive experiments show a clearly improved classification performance within the critical very first hours of a rumor. For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor lifetime.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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Social Informatics: 9th International Conference, SocInfo 2017, Proceedings. Hrsg. / Giovanni Luca Ciampaglia; Taha Yasseri; Afra Mashhadi. Cham: Springer Verlag, 2017. S. 141-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10540 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - On early-stage debunking rumors on twitter
T2 - 9th International Conference on Social Informatics, SocInfo 2017
AU - Nguyen, Tu Ngoc
AU - Li, Cheng
AU - Niederée, Claudia
N1 - Funding information:. This work was partially funded by the German Federal Ministry of Education and Research (BMBF) under project GlycoRec (16SV7172) and K3 (13N13548).
PY - 2017/9/2
Y1 - 2017/9/2
N2 - Recently a lot of progress has been made in rumor modeling and rumor detection for micro-blogging streams. However, existing automated methods do not perform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the early phase of a rumor. In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights on the credibility of each tweets. We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model. Our extensive experiments show a clearly improved classification performance within the critical very first hours of a rumor. For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor lifetime.
AB - Recently a lot of progress has been made in rumor modeling and rumor detection for micro-blogging streams. However, existing automated methods do not perform very well for early rumor detection, which is crucial in many settings, e.g., in crisis situations. One reason for this is that aggregated rumor features such as propagation features, which work well on the long run, are - due to their accumulating characteristic - not very helpful in the early phase of a rumor. In this work, we present an approach for early rumor detection, which leverages Convolutional Neural Networks for learning the hidden representations of individual rumor-related tweets to gain insights on the credibility of each tweets. We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model. Our extensive experiments show a clearly improved classification performance within the critical very first hours of a rumor. For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor lifetime.
UR - http://www.scopus.com/inward/record.url?scp=85029512587&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67256-4_13
DO - 10.1007/978-3-319-67256-4_13
M3 - Conference contribution
AN - SCOPUS:85029512587
SN - 9783319672557
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 141
EP - 158
BT - Social Informatics
A2 - Ciampaglia, Giovanni Luca
A2 - Yasseri, Taha
A2 - Mashhadi, Afra
PB - Springer Verlag
CY - Cham
Y2 - 13 September 2017 through 15 September 2017
ER -