TY - JOUR
T1 - Efficient and timely misinformation blocking under varying cost constraints
AU - Litou, Iouliana
AU - Kalogeraki, Vana
AU - Katakis, Ioannis
AU - Gunopulos, Dimitrios
N1 - Funding Information:
Vana Kalogeraki is an Associate Professor leading the Distributed and Real-Time Systems research at Athens University of Economics and Business. Previously she has held positions as an Associate and Assistant Professor at the Department of Computer Science at the University of California, Riverside and as a Research Scientist at Hewlett-Packard Labs in Palo Alto, CA. She received her PhD from the University of California, Santa Barbara in 2000. Prof. Vana Kalogeraki has been working in the field of distributed and real-time systems, participatory sensing systems, peer-to-peer systems, crowdsourcing, mobility, resource management and fault-tolerance for over 20 years and has authored and co-authored over 150 papers in journals and conferences proceedings, including co-authoring the OMG CORBA Dynamic Scheduling Standard. Prof. Kalogeraki was invited to give keynote talks at MoVid2015, DNCMS 2012, SN2AE 2012, PETRA 2011, DBISP2P 2006 and MLSN 2006 in the areas of participatory sensing systems and sensor network middleware and delivered tutorials and seminars on peer-to-peer computing. She has served as the General co-Chair of SEUS 2009, the General co-Chair of WPDRTS 2006 and as a Program co-Chair of MDM 2017, DEBS 2016, MDM 2011, ISORC 2009, ISORC 2007, ICPS 2005, WPDRTS 2005 and DBISP2P 2003, a Tutorial Chair for ACM DEBS 2015, a Workshops Chair for IEEE SRDS 2015, a Demo Chair for IEEE MDM 2012, in addition to other roles such as Area Chair (IEEE ICDCS 2016, 2012) and as program committee member on over 200 conferences. She was also awarded a Marie Curie Fellowship, three best paper awards at the 11th ACM International Conference on Distributed Event-Based Systems (DEBS 2016), 24th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2009) and the 9th IEEE Annual International Symposium on Applications and the Internet (SAINT 2008), a Best Student Paper Award at the 11th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT 2011), a UC Regents Fellowship Award, UC Academic Senate Research Awards and a research award from HP Labs. Her research has been supported by an ERC Starting Independent Researcher Grant, the European Union, joint EU/Greek “Aristeia” grant, a joint EU/Greek “Thalis” grant, NSF and gifts from SUN and Nokia.
Funding Information:
This research has been financed by the European Union through the FP7 ERC IDEAS 308019 NGHCS project, the Horizon2020 688380 VaVeL project and a Google 2017 Faculty Award.
Funding Information:
Dimitrios Gunopulos is a Professor at the Department of Informatics and Telecommunications, at the National and Kapodistrian University of Athens. He got his PhD from Princeton University in 1995. He has held positions as a Postoctoral Fellow at the Max-Planck-Institut for Informatics, Research Associate at the IBM Almaden Research Center, Visiting Researcher at the University of Helsinki, Assistant, Associate, and Full Professor at the Department of Computer Science and Engineering in the University of California Riverside, and Visiting Researcher in Microsoft Research, Silicon Valley. His research is in the areas of Data Mining, Knowledge Discovery in Databases, Databases, Sensor Networks, Peer-to-Peer systems, and Algorithms. He has co-authored over a hundred journal and conference papers that have been widely cited and a book. He has 11 Ph.D. students that have joined industry labs or have taken academic positions. His research has been supported by NSF (including an NSF CAREER award), the DoD, the Institute of Museum and Library Services, the Tobacco Related Disease Research Program, the European Commission, the General Secretariat of Research and Technology, AT&T, Nokia, the Stavros Niarchos Foundation, a Yahoo Faculty Award and a Google Faculty Award. He has served as a General co-Chair in SIAM SDM 2018, SIAM SDM 2017, HDMS 2011 and IEEE ICDM 2010, as a PC co-Chair in ECML/PKDD 2011, in IEEE ICDM 2008, in ACM SIGKDD 2006, in SSDBM 2003, and in DMKD 2000.
Funding Information:
This research has been financed by the European Union through the FP7 ERC IDEAS 308019 NGHCS project, the Horizon2020 688380 VaVeL project and a Google 2017 Faculty Award.
Publisher Copyright:
© 2017 Elsevier B.V.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2017/8
Y1 - 2017/8
N2 - Online Social Networks (OSNs) constitute one of the most important communication channels and are widely utilized as news sources. Information spreads widely and rapidly in OSNs through the word-of-mouth effect. However, it is not uncommon for misinformation to propagate in the network. Misinformation dissemination may lead to undesirable effects, especially in cases where the non-credible information concerns emergency events. Therefore, it is essential to timely limit the propagation of misinformation. Towards this goal, we suggest a novel propagation model, namely the Dynamic Linear Threshold (DLT) model, that effectively captures the way contradictory information, i.e., misinformation and credible information, propagates in the network. The DLT model considers the probability of a user alternating between competing beliefs, assisting in either the propagation of misinformation or credible news. Based on the DLT model, we formulate an optimization problem that under cost constraints aims in identifying the most appropriate subset of users to limit the spread of misinformation by initiating the propagation of credible information. We prove that our suggested approach achieves an approximation ratio of 1−1/e and demonstrate by experimental evaluation that it outperforms its competitors.
AB - Online Social Networks (OSNs) constitute one of the most important communication channels and are widely utilized as news sources. Information spreads widely and rapidly in OSNs through the word-of-mouth effect. However, it is not uncommon for misinformation to propagate in the network. Misinformation dissemination may lead to undesirable effects, especially in cases where the non-credible information concerns emergency events. Therefore, it is essential to timely limit the propagation of misinformation. Towards this goal, we suggest a novel propagation model, namely the Dynamic Linear Threshold (DLT) model, that effectively captures the way contradictory information, i.e., misinformation and credible information, propagates in the network. The DLT model considers the probability of a user alternating between competing beliefs, assisting in either the propagation of misinformation or credible news. Based on the DLT model, we formulate an optimization problem that under cost constraints aims in identifying the most appropriate subset of users to limit the spread of misinformation by initiating the propagation of credible information. We prove that our suggested approach achieves an approximation ratio of 1−1/e and demonstrate by experimental evaluation that it outperforms its competitors.
KW - Emergency events
KW - Misinformation blocking
KW - Social networks
UR - http://www.scopus.com/inward/record.url?scp=85062606767&partnerID=8YFLogxK
U2 - 10.1016/j.osnem.2017.07.001
DO - 10.1016/j.osnem.2017.07.001
M3 - Article
AN - SCOPUS:85062606767
SN - 2468-6964
VL - 2
SP - 19
EP - 31
JO - Online Social Networks and Media
JF - Online Social Networks and Media
ER -