TY - GEN
T1 - Mining false information on Twitter for a major disaster situation
AU - Nabeshima, Keita
AU - Mizuno, Junta
AU - Okazaki, Naoaki
AU - Inui, Kentaro
N1 - Funding Information:
This paper was partly supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grants No. 23240018 and 23700159 and by the Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST). We are grateful to Twitter Japan for its provision of invaluable data.
PY - 2014
Y1 - 2014
N2 - Social networking services (SNS), such as Twitter, disseminate not only useful information, but also false information. Identifying this false information is crucial in order to keep the information on a SNS reliable. The aim of this paper is to develop a method of extracting false information from among a large collection of tweets. We do so by using a set of linguistic patterns formulated to correct false information. More specifically, the proposed method extracts text passages that match specified correction patterns, clusters the passages into topics of false information, and selects a passage that represents each topic of false information. In the experiment we conduct, we build an evaluation set manually, and demonstrate the effectiveness of the proposed method.
AB - Social networking services (SNS), such as Twitter, disseminate not only useful information, but also false information. Identifying this false information is crucial in order to keep the information on a SNS reliable. The aim of this paper is to develop a method of extracting false information from among a large collection of tweets. We do so by using a set of linguistic patterns formulated to correct false information. More specifically, the proposed method extracts text passages that match specified correction patterns, clusters the passages into topics of false information, and selects a passage that represents each topic of false information. In the experiment we conduct, we build an evaluation set manually, and demonstrate the effectiveness of the proposed method.
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U2 - 10.1007/978-3-319-09912-5_9
DO - 10.1007/978-3-319-09912-5_9
M3 - Conference contribution
AN - SCOPUS:84905393764
SN - 9783319099118
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 96
EP - 109
BT - Active Media Technology - 10th International Conference, AMT 2014, Proceedings
PB - Springer Verlag
T2 - 10th International Conference on Active Media Technology, AMT 2014
Y2 - 11 August 2014 through 14 August 2014
ER -