Luigi Di Caro
Claudio Schifanella
Mario Cataldi
ABSTRACT
This seventh edition of the workshop aims at bringing together academics and practitioners from different areas to promote the vision of social media as social sensors.
Nowadays, Social media platforms represent freely-accessible information networks allowing registered (and unregistered) users to read, share and broadcast messages referring to a potentially-unlimited range of arguments, by also exploiting the immediateness of handy smart devices. This long-running workshop aims at focusing the attention on a particular perspective of these powerful communication channels, which is that of social sensors, where each user reacts in real time to the underlying reality by providing some own interpretation.
Technologies and AI artifacts may support automatic or semi-automatic applications for information detection and integration, offering sideways to the existing authoritative information media and the information reported by the surrounding community.
- Satyen Abrol and Latifur Khan. 2010. TWinner: understanding news queries with geo-content using Twitter. In GIR ’10: Proceedings of the 6th Workshop on Geographic Information Retrieval (Zurich, Switzerland). ACM, New York, NY, USA, 1–8.Google Scholar
Digital Library
- Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining (Barcelona, Spain) (WSDM ’09). ACM, New York, NY, USA, 5–14. https://doi.org/10.1145/1498759.1498766Google ScholarDigital Library
- Loulwah AlSumait, Daniel Barbará, and Carlotta Domeniconi. 2008. On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining(ICDM ’08). IEEE Computer Society, Washington, DC, USA, 3–12. https://doi.org/10.1109/ICDM.2008.140Google ScholarDigital Library
- Sitaram Asur, Bernardo A. Huberman, Gábor Szabó, and Chunyan Wang. 2011. Trends in Social Media: Persistence and Decay. In Fifth International AAAI Conference on Weblogs and Social Media. The AAAI Press, Menlo Park, California.Google Scholar
- Marko Balabanovic and Yoav Shoham. 1997. Fab: Content-based, collaborative recommendation. Commun. ACM 40(1997), 66–72.Google ScholarDigital Library
- A. L. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, and T. Vicsek. 2002. Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications 311, 3-4(2002), 590–614.Google Scholar
- Hila Becker, Mor Naaman, and Luis Gravano. 2010. Learning similarity metrics for event identification in social media. In WSDM. ACM, New York, NY, USA, 291–300.Google Scholar
- H. Becker, M. Naaman, and L. Gravano. 2011. Beyond trending topics: Real-world event identification on Twitter. In Fifth International AAAI Conference on Weblogs and Social Media. The AAAI Press, Menlo Park, California.Google Scholar
- Khoo Khyou Bun, Mitsuru Ishizuka, and Bun Mitsuru Ishizuka. 2002. Topic Extraction from News Archive Using TF*PDF Algorithm. In Proceedings of 3rd Int’l Conference on Web Informtion Systems Engineering (WISE 2002), IEEE Computer Soc. IEEE Computer Society, Washington, DC, USA, 73–82.Google ScholarDigital Library
- K Selçuk Candan, Luigi Di Caro, and Maria Luisa Sapino. 2012. PhC: Multiresolution visualization and exploration of text corpora with parallel hierarchical coordinates. ACM Transactions on Intelligent Systems and Technology (TIST) 3, 2(2012), 1–36.Google ScholarDigital Library
- Iván Cantador, Alejandro Bellogín, and David Vallet. 2010. Content-based recommendation in social tagging systems. In Proceedings of the fourth ACM conference on Recommender systems (Barcelona, Spain) (RecSys ’10). ACM, New York, NY, USA, 237–240. https://doi.org/10.1145/1864708.1864756Google ScholarDigital Library
- David Carmel, Naama Zwerdling, Ido Guy, Shila Ofek-Koifman, Nadav Har’el, Inbal Ronen, Erel Uziel, Sivan Yogev, and Sergey Chernov. 2009. Personalized social search based on the user’s social network. In Proceedings of the 18th ACM conference on Information and knowledge management (Hong Kong, China) (CIKM ’09). ACM, New York, NY, USA, 1227–1236. https://doi.org/10.1145/1645953.1646109Google ScholarDigital Library
- Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and Krishna P. Gummadi. 2010. Measuring User Influence in Twitter: The Million Follower Fallacy. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM)(Washington, DC, USA). The AAAI Press, Menlo Park, California, 10–17.Google ScholarCross Ref
- Chien Chin Chen, Yao-Tsung Chen, Yeali S. Sun, and Meng Chang Chen. 2003. Life Cycle Modeling of News Events Using Aging Theory. In ECML. Springer, Berlin, Heidelberg, 47–59.Google Scholar
- Jilin Chen, Werner Geyer, Casey Dugan, Michael Muller, and Ido Guy. 2009. Make new friends, but keep the old: recommending people on social networking sites. In CHI ’09: Proceedings of the 27th international conference on Human factors in computing systems (Boston, MA, USA). ACM, New York, NY, USA, 201–210. https://doi.org/10.1145/1518701.1518735Google ScholarDigital Library
- Jilin Chen, Rowan Nairn, Les Nelson, Michael Bernstein, and Ed Chi. 2010. Short and tweet: experiments on recommending content from information streams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI ’10). ACM, New York, NY, USA, 1185–1194. https://doi.org/10.1145/1753326.1753503Google ScholarDigital Library
- Daryl E. Chubin. 1976. The Conceptualization of Scientific Specialties. The Sociological Quarterly 17, 4 (1976), 448–476.Google ScholarCross Ref
- D. Crane. 1969. Social Structure in a Group of Scientists: A Test of the “Invisible College” Hypothesis. American Sociological Review 3 (1969), 335–352.Google ScholarCross Ref
- D. de Beaver and R. Rosen. 1979. Studies in scientific collaboration. Scientometrics 1, 2 (1979), 133–149.Google ScholarCross Ref
- Luigi Di Caro, K Selçuk Candan, and Maria Luisa Sapino. 2011. Navigating within news collections using tag-flakes. Journal of Visual Languages & Computing 22, 2 (2011), 120–139.Google ScholarDigital Library
- Luigi Di Caro, Mario Cataldi, and Claudio Schifanella. 2012. The d-index: Discovering dependences among scientific collaborators from their bibliographic data records. Scientometrics (2012), 1–25. https://doi.org/10.1007/s11192-012-0762-1Google Scholar
- Alfredo Favenza, Mario Cataldi, Maria Luisa Sapino, and Alberto Messina. 2008. Topic Development Based Refinement of Audio-Segmented Television News. In NLDB ’08(London, UK). Springer-Verlag, Berlin, Heidelberg, 226–232. https://doi.org/10.1007/978-3-540-69858-6_23Google ScholarDigital Library
- Susan Gauch, Jason Chaffee, and Alaxander Pretschner. 2003. Ontology-based personalized search and browsing. Web Intelligence and Agent Systems 1 (December 2003), 219–234. Issue 3-4. http://dl.acm.org/citation.cfm?id=1016416.1016421Google Scholar
- David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. 1992. Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 12 (1992), 61–70.Google ScholarDigital Library
- J. Goldenberg, B. Libai, and E. Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing letters 12, 3 (2001), 211–223.Google Scholar
- Amit Goyal, Francesco Bonchi, and Laks V.S. Lakshmanan. 2010. Learning influence probabilities in social networks. In Proceedings of the third ACM international conference on Web search and data mining (New York, New York, USA) (WSDM ’10). ACM, New York, NY, USA, 241–250. https://doi.org/10.1145/1718487.1718518Google ScholarDigital Library
- Mark Granovetter. 1978. Threshold Models of Collective Behavior. Amer. J. Sociology 83, 6 (1978), 1420–1443. https://doi.org/doi:10.1086/226707Google ScholarCross Ref
- T. L. Griffiths and M. Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences 101, Suppl. 1 (April 2004), 5228–5235.Google Scholar
- Daniel Gruhl, R. Guha, David Liben-Nowell, and Andrew Tomkins. 2004. Information diffusion through blogspace. In Proceedings of the 13th international conference on World Wide Web (New York, NY, USA) (WWW ’04). ACM, New York, NY, USA, 491–501. https://doi.org/10.1145/988672.988739Google ScholarDigital Library
- Xiaogang Han, Zhiqi Shen, Chunyan Miao, and Xudong Luo. 2010. Folksonomy-based ontological user interest profile modeling and its application in personalized search. In Proceedings of the 6th international conference on Active media technology (Toronto, Canada) (AMT’10). Springer-Verlag, Berlin, Heidelberg, 34–46. http://dl.acm.org/citation.cfm?id=1886192.1886201Google ScholarDigital Library
- Ahmed Hassan, Dragomir R. Radev, Junghoo Cho, and Amruta Joshi. 2009. Content Based Recommendation and Summarization in the Blogosphere. In Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media (ICWSM) (San Jose, California, USA). The AAAI Press, Menlo Park, California, 34–41.Google ScholarCross Ref
- Qi He, Kuiyu Chang, and Ee-Peng Lim. 2007. Using Burstiness to Improve Clustering of Topics in News Streams. Data Mining, IEEE International Conference on 0 (2007), 493–498. https://doi.org/10.1109/ICDM.2007.17Google ScholarDigital Library
- H. Hou, H. Kretschmer, and Z. Liu. 2008. The structure of scientific collaboration networks in Scientometrics. Scientometrics 75, 2 (2008), 189–202.Google ScholarCross Ref
- Robert Jäschke, Leandro Marinho, Andreas Hotho, Lars Schmidt-Thieme, and Gerd Stumme. 2007. Tag Recommendations in Folksonomies. In PKDD 2007 (Warsaw, Poland). Springer-Verlag, Berlin, Heidelberg, 506–514. https://doi.org/10.1007/978-3-540-74976-9_52Google Scholar
- J. Sylvan Katz, J. Sylvan Katz, Ben R. Martin, and Ben R. Martin. 1997. What is Research Collaboration?Research Policy 26(1997), 1–18.Google Scholar
- Vasileios Lampos and Nello Cristianini. 2012. Nowcasting Events from the Social Web with Statistical Learning. ACM Trans. Intell. Syst. Technol. 3, 4, Article 72 (Sept. 2012), 22 pages. https://doi.org/10.1145/2337542.2337557Google ScholarDigital Library
- Gu-Li Lin, Hong Peng, Qian-Li Ma, Jia Wei, and Jiang-Wei Qin. 2010. Improving diversity in Web search results re-ranking using absorbing random walks. In Machine Learning and Cybernetics (ICMLC), 2010 International Conference on, Vol. 5. IEEE Computer Society, Washington, DC, USA, 2116 –2421. https://doi.org/10.1109/ICMLC.2010.5580733Google ScholarCross Ref
- G. Melin and O. Persson. 1996. Studying research collaboration using co-authorships. Scientometrics 36(1996), 363–377. Issue 3.Google ScholarCross Ref
- Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan. 2001. Content-Boosted Collaborative Filtering. In In Proceedings of the 2001 SIGIR Workshop on Recommender Systems (New Orleans, LA, USA). ACM, New York, NY, USA, 16–23.Google Scholar
- S. Moon, J. You, H. Kwak, D. Kim, and H. Jeong. 2010. Understanding topological mesoscale features in community mining. In Communication Systems and Networks (COMSNETS), 2010 Second International Conference on. IEEE Press, Piscataway, NJ, USA, 1–10.Google Scholar
- M. E. J. Newman. 2001. Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E 64, 1 (June 2001). http://dx.doi.org/10.1103/PhysRevE.64.016131Google ScholarCross Ref
- Michael G. Noll and Christoph Meinel. 2007. Web search personalization via social bookmarking and tagging. In Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference (Busan, Korea) (ISWC’07/ASWC’07). Springer-Verlag, Berlin, Heidelberg, 367–380. http://dl.acm.org/citation.cfm?id=1785162.1785190Google Scholar
- Saša Petrović, Miles Osborne, and Victor Lavrenko. 2010. Streaming first story detection with application to Twitter. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Los Angeles, California) (HLT ’10). Association for Computational Linguistics, Stroudsburg, PA, USA, 181–189. http://dl.acm.org/citation.cfm?id=1857999.1858020Google ScholarDigital Library
- Yan Qi and K. Selçuk Candan. 2006. CUTS: CUrvature-based development pattern analysis and segmentation for blogs and other Text Streams. In HYPERTEXT ’06 (Odense, Denmark). ACM, New York, NY, USA, 1–10. https://doi.org/10.1145/1149941.1149944Google ScholarDigital Library
- Filip Radlinski and Susan Dumais. 2006. Improving personalized web search using result diversification. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (Seattle, Washington, USA) (SIGIR ’06). ACM, New York, NY, USA, 691–692. https://doi.org/10.1145/1148170.1148320Google ScholarDigital Library
- J. Sankaranarayanan, H. Samet, B.E. Teitler, M.D. Lieberman, and J. Sperling. 2009. Twitterstand: news in tweets. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, ACM, New York, NY, USA, 42–51.Google Scholar
- Claudio Schifanella, Luigi Di Caro, Mario Cataldi, and Marie-Aude Aufaure. 2012. The D-INDEX: a web environment for analyzing dependences among scientific collaborators. In KDD. ACM, New York, NY, USA, 1520–1523.Google Scholar
- Steven Shapin. 1981. Laboratory life. The social construction of scientific facts. Medical History 25, 3 (1981), 341–342.Google ScholarCross Ref
- Ahu Sieg, Bamshad Mobasher, and Robin Burke. 2007. Web search personalization with ontological user profiles. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (Lisbon, Portugal) (CIKM ’07). ACM, New York, NY, USA, 525–534. https://doi.org/10.1145/1321440.1321515Google ScholarDigital Library
- Bharath Sriram, Dave Fuhry, Engin Demir, Hakan Ferhatosmanoglu, and Murat Demirbas. 2010. Short text classification in twitter to improve information filtering. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (Geneva, Switzerland) (SIGIR ’10). ACM, New York, NY, USA, 841–842. https://doi.org/10.1145/1835449.1835643Google ScholarDigital Library
- Kazunari Sugiyama, Kenji Hatano, and Masatoshi Yoshikawa. 2004. Adaptive web search based on user profile constructed without any effort from users. In Proceedings of the 13th international conference on World Wide Web (New York, NY, USA) (WWW ’04). ACM, New York, NY, USA, 675–684. https://doi.org/10.1145/988672.988764Google ScholarDigital Library
- Makoto Okazaki Takeshi Sakaki and Yutaka Matsuo. 2010. Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. In WWW 2010(Raleigh, NC, USA). ACM, New York, NY, USA.Google Scholar
- J. Teevan, S.T. Dumais, and E. Horvitz. 2005a. Beyond the commons: Investigating the value of personalizing web search. In Proceedings of the Workshop on New Technologies for Personalized Information Access (PIA). 84–92.Google Scholar
- Jaime Teevan, Susan T. Dumais, and Eric Horvitz. 2005b. Personalizing search via automated analysis of interests and activities. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (Salvador, Brazil) (SIGIR ’05). ACM, New York, NY, USA, 449–456. https://doi.org/10.1145/1076034.1076111Google ScholarDigital Library
- Pucktada Treeratpituk and Jamie Callan. 2006. Automatically labeling hierarchical clusters. In dg.o ’06: Proceedings of the 2006 international conference on Digital government research (San Diego, California). ACM, New York, NY, USA, 167–176. https://doi.org/10.1145/1146598.1146650Google ScholarDigital Library
- Canhui Wang, Min Zhang, Liyun Ru, and Shaoping Ma. 2008. Automatic online news topic ranking using media focus and user attention based on aging theory. In CIKM ’08 (Napa Valley, California, USA). ACM, New York, NY, USA, 1033–1042. https://doi.org/10.1145/1458082.1458219Google ScholarDigital Library
- Qihua Wang and Hongxia Jin. 2010. Exploring online social activities for adaptive search personalization. In Proceedings of the 19th ACM international conference on Information and knowledge management(Toronto, ON, Canada) (CIKM ’10). ACM, New York, NY, USA, 999–1008. https://doi.org/10.1145/1871437.1871564Google ScholarDigital Library
- Steve Wedig and Omid Madani. 2006. A large-scale analysis of query logs for assessing personalization opportunities. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (Philadelphia, PA, USA) (KDD ’06). ACM, New York, NY, USA, 742–747. https://doi.org/10.1145/1150402.1150497Google ScholarDigital Library
- Yonghui Wu, Yuxin Ding, Xiaolong Wang, and Jun Xu. 2010. On-line Hot Topic Recommendation Using Tolerance Rough Set Based Topic Clustering. Journal of Computers 5, 4 (2010), 549–556.Google ScholarCross Ref
- Shengliang Xu, Shenghua Bao, Ben Fei, Zhong Su, and Yong Yu. 2008. Exploring folksonomy for personalized search. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval(Singapore, Singapore) (SIGIR ’08). ACM, New York, NY, USA, 155–162. https://doi.org/10.1145/1390334.1390363Google ScholarDigital Library
- J. Yang and S. Counts. 2010. Predicting the speed, scale, and range of information diffusion in twitter. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM) (Washington, DC, USA). The AAAI Press, Menlo Park, California, 355–358.Google Scholar
- J. Yang and J. Leskovec. 2010. Modeling information diffusion in implicit networks. In Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE Computer Society, Washington, DC, USA, 599–608.Google Scholar
- Qiankun Zhao, Prasenjit Mitra, and Bi Chen. 2007. Temporal and information flow based event detection from social text streams. In Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (Vancouver, British Columbia, Canada) (AAAI’07). AAAI Press, Menlo Park, California, 1501–1506. http://dl.acm.org/citation.cfm?id=1619797.1619886Google Scholar
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web (Chiba, Japan) (WWW ’05). ACM, New York, NY, USA, 22–32. https://doi.org/10.1145/1060745.1060754Google ScholarDigital Library
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