Symeon Papadopoulos
Kalina Bontcheva
Mihai Lupu
Carlos Castillo
- Janna Anderson and Lee Rainie. 2012. The future of big data. Retrieved December 17, 2015 from http://www.pewinternet.org/2012/07/20/the-future-of-big-data/.Google Scholar
- Alessandro Bessi, Mauro Coletto, George A. Davidescu, Antonio Scala, Guido Caldarelli, and Walter Quattrociocchi. 2015. Science vs conspiracy: Collective narratives in the age of misinformation. PLOS ONE 10, 2 (23 Feb. 2015), e0118093+. DOI:http://dx.doi.org/10.1371/journal.pone.0118093Google Scholar
- Christina Boididou, Katerina Andreadou, Symeon Papadopoulos, Duc-Tien Dang-Nguyen, Giulia Boato, Michael Riegler, and Yiannis Kompatsiaris. 2015. Verifying multimedia use at MediaEval 2015. In Working Notes Proceedings of the MediaEval 2015 Workshop.Google Scholar
- Christina Boididou, Symeon Papadopoulos, Yiannis Kompatsiaris, Steve Schifferes, and Nic Newman. 2014. Challenges of computational verification in social multimedia. In Proceedings of the 23rd International Conference on World Wide Web (WWW’14 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 743--748. DOI:http://dx.doi.org/10.1145/2567948.2579323 Google ScholarDigital Library
- Kevin Robert Canini, Bongwon Suh, and Peter Pirolli. 2011. Finding credible information sources in social networks based on content and social structure. In Proceedings of the 2011 IEEE 3rd International Conference on Privacy, Security, Risk and Trust and 2011 IEEE 3rd International Conference on Social Computing. IEEE, 1--8. DOI:http://dx.doi.org/10.1109/PASSAT/SocialCom.2011.91Google ScholarCross Ref
- Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on twitter. In Proceedings of the 20th International Conference on World Wide Web (WWW’11). ACM, New York, NY, 675--684. DOI:http://dx.doi.org/10.1145/1963405.1963500 Google ScholarDigital Library
- Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2013. Predicting information credibility in time-sensitive social media. Internet Research 23, 5 (2013), 560--588. DOI:http://dx.doi.org/10.1108/IntR-05-2012-0095Google ScholarCross Ref
- Dennis Fetterly and Zoltán Gyöngyi (Eds.). 2009. Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web. Google ScholarCross Ref
- Aditi Gupta and Ponnurangam Kumaraguru. 2012. Credibility ranking of tweets during high impact events. In Proceedings of the 1st Workshop on Privacy and Security in Online Social Media (PSOSM’12). ACM, New York, NY, Article 2, 7 pages. DOI:http://dx.doi.org/10.1145/2185354.2185356 Google ScholarDigital Library
- Aditi Gupta, Ponnurangam Kumaraguru, Carlos Castillo, and Patrick Meier. 2014. TweetCred: Real-time credibility assessment of content on twitter. In Proceedings of the 6th International Conference, SocInfo 2014. Luca Maria Aiello and Daniel A. McFarland (Eds.), Vol. 8851. Springer, 228--243. DOI:http://dx.doi.org/10.1007/978-3-319-13734-6_16Google ScholarCross Ref
- Aditi Gupta, Hemank Lamba, Ponnurangam Kumaraguru, and Anupam Joshi. 2013. Faking Sandy: Characterizing and identifying fake images on Twitter during hurricane Sandy. In Proceedings of the 22nd International Conference on World Wide Web (WWW’13 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 729--736. Google ScholarDigital Library
- Bogdan Ionescu, Alexandru-Lucian Gînsca, Bogdan Boteanu, Adrian Popescu, Mihai Lupu, and Henning Müller. 2015. Retrieving diverse social images at MediaEval 2015: Challenge, dataset and evaluation. In Working Notes Proceedings of the MediaEval 2015 Workshop. Martha A. Larson, Bogdan Ionescu, Mats Sjöberg, Xavier Anguera, Johann Poignant, Michael Riegler, Maria Eskevich, Claudia Hauff, Richard F. E. Sutcliffe, Gareth J. F. Jones, Yi-Hsuan Yang, Mohammad Soleymani, and Symeon Papadopoulos (Eds.). 2015. Working Notes Proceedings of the MediaEval 2015 Workshop. CEUR Workshop Proceedings, Vol. 1436Google Scholar
- Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li, Rui Fang, and Sameena Shah. 2015. Real-time rumor debunking on Twitter. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM’15). ACM, New York, NY, 1867--1870. DOI:http://dx.doi.org/10.1145/2806416.2806651 Google ScholarDigital Library
- Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, and Kam-Fai Wong. 2015. Detect rumors using time series of social context information on microblogging websites. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM’15). ACM, New York, NY, 1751--1754. DOI:http://dx.doi.org/10.1145/2806416.2806607 Google ScholarDigital Library
- Juan Martinez-Romo and Lourdes Araujo. 2013. Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Syst. Appl. 40, 8 (June 2013), 2992--3000. DOI:http://dx.doi.org/10.1016/j.eswa.2012.12.015 Google ScholarDigital Library
- Radoslaw Nielek, Adam Wierzbicki, Adam Jatowt, and Katsumi Tanaka (Eds.). 2015. WebQuality 2015, 5th International Workshop on Web Quality, Co-Located with the 24th International World Wide Web Conference (WWW’15).Google Scholar
- John O’Donovan, Byungkyu Kang, Greg Meyer, Tobias Hollerer, and Sibel Adalii. 2012. Credibility in context: An analysis of feature distributions in twitter. In Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust (SOCIALCOM-PASSAT’12). IEEE Computer Society, Washington, DC, 293--301. DOI:http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.128 Google ScholarDigital Library
- Symeon Papadopoulos, David Corney, and Luca Maria Aiello (Eds.). 2014. Proceedings of the SNOW 2014 Data Challenge co-located with the 23rd International World Wide Web Conference (WWW’14). CEUR Workshop Proceedings, Vol. 1150.Google Scholar
- Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, and Qiaozhu Mei. 2011. Rumor has it: Identifying misinformation in microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’11). Association for Computational Linguistics, Stroudsburg, PA, 1589--1599. Google ScholarDigital Library
- Jacob Ratkiewicz, Michael Conover, Mark Meiss, Bruno Gonçalves, Snehal Patil, Alessandro Flammini, and Filippo Menczer. 2011. Truthy: Mapping the spread of astroturf in microblog streams. In Proceedings of the 20th International Conference Companion on World Wide Web (WWW’11). ACM, New York, NY, 249--252. DOI:http://dx.doi.org/10.1145/1963192.1963301 Google ScholarDigital Library
- Matthew Rowe, Milan Stankovic, and Aba-Sah Dadzie (Eds.). 2015. Proceedings of the the 5th Workshop on Making Sense of Microposts Co-Located with the 24th International World Wide Web Conference (WWW’15). CEUR Workshop Proceedings, Vol. 1395.Google Scholar
- Eunsoo Seo, Prasant Mohapatra, and Tarek Abdelzaher. 2012. Identifying rumors and their sources in social networks. Proc. SPIE 8389 (2012), 83891I--83891I--13. DOI:http://dx.doi.org/10.1117/12.919823Google ScholarCross Ref
- Katsumi Tanaka, Xiaofang Zhou, Min Zhang, and Adam Jatowt (Eds.). 2010. Proceedings of the 4th ACM Workshop on Information Credibility on the Web (WICOW’10). Google Scholar
- Arkaitz Zubiaga, Maria Liakata, Rob Procter, Kalina Bontcheva, and Peter Tolmie. 2015. Towards detecting rumours in social media. CoRR abs/1504.04712 (2015). http://arxiv.org/abs/1504.04712Google Scholar
- Arkaitz Zubiaga, Damiano Spina, Maarten de Rijke, and Markus Strohmaier (Eds.). 2013. RAMSS 2013: Proceedings of the Second Workshop on Real-time Analysis and Mining of Social Streams, Co-Located with the 22nd International World Wide Web Conference (WWW’13).Google Scholar
Index Terms
- Overview of the Special Issue on Trust and Veracity of Information in Social Media
Recommendations
Impersonation on social media: a deep neural approach to identify ingenuine content
ASONAM '20: Proceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningImpersonators are playing an important role in the production and propagation of the content on Online Social Networks, notably on Instagram. These entities are nefarious fake accounts that intend to disguise a legitimate account by making similar ...
A Study of Tweet Veracity to Separate Rumours from Counter-Rumours
#SMSociety17: Proceedings of the 8th International Conference on Social Media & SocietyRumours are known to propagate easily through computer-mediated communication channels such as Twitter. Their outbreak is often followed by the spread of 'counter-rumours', which are messages that debunk rumours. The probability of a tweet to be a ...
On the influence blocking maximization for minimizing the spreading of fake information in social media
SpringSim '20: Proceedings of the 2020 Spring Simulation ConferenceInfluence can be used to propagate the (fake or true) information in social media where a set of influential nodes (individuals) in social media can leverage their connections (e.g., followers in Tweeter) to impact others. Lately, most of the social ...
Comments