TY - GEN
T1 - Mood-sensitive Truth Discovery for Reliable Recommendation Systems in Social Sensing
AU - Marshall, Jermaine
AU - Wang, Dong
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. CBET-1637251, CNS-1566465 and IIS-1447795 and Army Research Office under Grant 69595-CS-II.
Publisher Copyright:
© 2016 ACM.
PY - 2016/9/7
Y1 - 2016/9/7
N2 - This work is motivated by the need to provide reliable information recommendation to users in social sensing. Social sensing has become an emerging application paradigm that uses humans as sensors to observe and report events in the physical world. These human sensed observations are often viewed as binary claims (either true or false). A fundamental challenge in social sensing is how to ascertain the credibility of claims and the reliability of sources without knowing either of them a priori. We refer to this challenge as truth discovery. While prior works have made progress on addressing this challenge, an important limitation exists: they did not explore the mood sensitivity aspect of the problem. There- fore, the claims identified as correct by current solutions can be completely biased in regards to the mood of human sources and lead to useless or even misleading recommendations. In this paper, we present a new analytical model that explicitly considers the mood sensitivity feature in the solution of truth discovery problem. The new model solves a multi-dimensional estimation problem to jointly estimate the correctness and mood neutrality of claims as well as the reliability and mood sensitivity of sources. We compare our model with state-of-the-art truth discovery solutions us- ing four real world datasets collected from Twitter during recent disastrous and emergent events: Brussels Bombing, Paris Attack, Oregon Shooting, Baltimore Riots, which occurred in 2015 and 2016. The results show that our model has significant improvements over the compared baselines by finding more correct and mood neutral claims.
AB - This work is motivated by the need to provide reliable information recommendation to users in social sensing. Social sensing has become an emerging application paradigm that uses humans as sensors to observe and report events in the physical world. These human sensed observations are often viewed as binary claims (either true or false). A fundamental challenge in social sensing is how to ascertain the credibility of claims and the reliability of sources without knowing either of them a priori. We refer to this challenge as truth discovery. While prior works have made progress on addressing this challenge, an important limitation exists: they did not explore the mood sensitivity aspect of the problem. There- fore, the claims identified as correct by current solutions can be completely biased in regards to the mood of human sources and lead to useless or even misleading recommendations. In this paper, we present a new analytical model that explicitly considers the mood sensitivity feature in the solution of truth discovery problem. The new model solves a multi-dimensional estimation problem to jointly estimate the correctness and mood neutrality of claims as well as the reliability and mood sensitivity of sources. We compare our model with state-of-the-art truth discovery solutions us- ing four real world datasets collected from Twitter during recent disastrous and emergent events: Brussels Bombing, Paris Attack, Oregon Shooting, Baltimore Riots, which occurred in 2015 and 2016. The results show that our model has significant improvements over the compared baselines by finding more correct and mood neutral claims.
KW - Disaster and Emergency Response
KW - Mood Sensitive
KW - Reliable Recommendation Systems
KW - Social Sensing
KW - Truth Discovery
UR - http://www.scopus.com/inward/record.url?scp=84991261868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991261868&partnerID=8YFLogxK
U2 - 10.1145/2959100.2959147
DO - 10.1145/2959100.2959147
M3 - Conference contribution
AN - SCOPUS:84991261868
T3 - RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
SP - 167
EP - 174
BT - RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
PB - Association for Computing Machinery
T2 - 10th ACM Conference on Recommender Systems, RecSys 2016
Y2 - 15 September 2016 through 19 September 2016
ER -