skip to main content
10.1145/3511095.3532573acmconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
extended-abstract

SIDEWAYS-2022 @ HT-2022: 7th International Workshop on Social Media World Sensors

Published: 28 June 2022 Publication History

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.

References

[1]
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.
[2]
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/10.1145/1498759.1498766
[3]
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.2008.140
[4]
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.
[5]
Marko Balabanovic and Yoav Shoham. 1997. Fab: Content-based, collaborative recommendation. Commun. ACM 40(1997), 66–72.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
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/10.1145/1864708.1864756
[12]
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/10.1145/1645953.1646109
[13]
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.
[14]
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.
[15]
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/10.1145/1518701.1518735
[16]
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/10.1145/1753326.1753503
[17]
Daryl E. Chubin. 1976. The Conceptualization of Scientific Specialties. The Sociological Quarterly 17, 4 (1976), 448–476.
[18]
D. Crane. 1969. Social Structure in a Group of Scientists: A Test of the “Invisible College” Hypothesis. American Sociological Review 3 (1969), 335–352.
[19]
D. de Beaver and R. Rosen. 1979. Studies in scientific collaboration. Scientometrics 1, 2 (1979), 133–149.
[20]
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.
[21]
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/s11192-012-0762-1
[22]
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_23
[23]
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=1016416.1016421
[24]
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.
[25]
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.
[26]
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/10.1145/1718487.1718518
[27]
Mark Granovetter. 1978. Threshold Models of Collective Behavior. Amer. J. Sociology 83, 6 (1978), 1420–1443. https://doi.org/
[28]
T. L. Griffiths and M. Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences 101, Suppl. 1 (April 2004), 5228–5235.
[29]
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/10.1145/988672.988739
[30]
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=1886192.1886201
[31]
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.
[32]
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.2007.17
[33]
H. Hou, H. Kretschmer, and Z. Liu. 2008. The structure of scientific collaboration networks in Scientometrics. Scientometrics 75, 2 (2008), 189–202.
[34]
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_52
[35]
J. Sylvan Katz, J. Sylvan Katz, Ben R. Martin, and Ben R. Martin. 1997. What is Research Collaboration?Research Policy 26(1997), 1–18.
[36]
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/10.1145/2337542.2337557
[37]
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.2010.5580733
[38]
G. Melin and O. Persson. 1996. Studying research collaboration using co-authorships. Scientometrics 36(1996), 363–377. Issue 3.
[39]
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.
[40]
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.
[41]
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.64.016131
[42]
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=1785162.1785190
[43]
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=1857999.1858020
[44]
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/10.1145/1149941.1149944
[45]
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/10.1145/1148170.1148320
[46]
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.
[47]
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.
[48]
Steven Shapin. 1981. Laboratory life. The social construction of scientific facts. Medical History 25, 3 (1981), 341–342.
[49]
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/10.1145/1321440.1321515
[50]
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/10.1145/1835449.1835643
[51]
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/10.1145/988672.988764
[52]
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.
[53]
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.
[54]
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/10.1145/1076034.1076111
[55]
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/10.1145/1146598.1146650
[56]
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/10.1145/1458082.1458219
[57]
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/10.1145/1871437.1871564
[58]
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/10.1145/1150402.1150497
[59]
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.
[60]
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/10.1145/1390334.1390363
[61]
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.
[62]
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.
[63]
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=1619797.1619886
[64]
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/10.1145/1060745.1060754

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
HT '22: Proceedings of the 33rd ACM Conference on Hypertext and Social Media
June 2022
272 pages
ISBN:9781450392334
DOI:10.1145/3511095
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2022

Check for updates

Author Tags

  1. data mining
  2. sensors
  3. social media
  4. topic detection

Qualifiers

  • Extended-abstract
  • Research
  • Refereed limited

Conference

HT '22
Sponsor:
HT '22: 33rd ACM Conference on Hypertext and Social Media
June 28 - July 1, 2022
Barcelona, Spain

Acceptance Rates

Overall Acceptance Rate 378 of 1,158 submissions, 33%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 32
    Total Downloads
  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media