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Social media mining

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Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services).

Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data.[1] In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens.

Background

As defined by Kaplan and Haenlein,[2] social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World ofWarcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger).

The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.

Research

Research areas

  • Community structure (Community Detection/Evolution/Evaluation) – Identifying communities on social networks, how they evolve, and evaluating identified communities, often without ground truth.[1]
  • Network measures – Measuring centrality, transitivity, reciprocity, balance, status, and similarity in social media.[1]
  • Network models – Simulate networks with specific characteristics. Examples include random graphs (E-R models), Preferential attachment models, and small-world models.[1]
  • Information cascade – Analyzing how information propagates in social media sites. Examples include herd behavior, information cascades, diffusion of innovations, and epidemic models.[1]
  • Influence and homophily – Measuring network assortativity and measuring and modeling influence and homophily.[1]
  • Recommendation in social media – recommending friends or items on social media sites.[1][3][4]
  • Social search – Searching for information on the social web.[5]
  • Sentiment analysis in social media – Identifying collectively subjective information, e.g. positive and negative, from social media data.[6][7]
  • Social spammer detection – Detecting social spammers who send out unwanted spam content appearing on social networks and any website with user-generated content to targeted users, often corroborating to boost their social influence, legitimacy, credibility.[8][9][10][11]
  • Feature selection with social media data – Transforming feature selection to harness the power of social media.[12][13][14][15]
  • Trust in social media – Studying and understanding of trust in social media.[16][17][18][19]
  • Distrust and negative links – Exploring negative links in social media.[20][21][22]
  • Role of social media in crises – Social media is continuing to play an important role during crises, particularly Twitter.[23] Studies show that it is possible to detect earthquakes[24] and rumors[25] using tweets published during crisis. Developing tools to help first responders to analyze tweets towards better crisis response[26] and developing techniques to provide them faster access to relevant tweets[27] is an active area of research.
  • Location-based social network mining – Mining Human Mobility for Personalized POI Recommendation on Location-based Social Networks.[28][29][30][31][32][33]
  • Provenance of information in social media – Provenance informs a user about the sources of a given piece of information. Social media can help in identifying the provenance of information due its unique features: user-generated content, user profiles, user interactions, and spatial or temporal information.[34][35]
  • Vulnerability management – A user's vulnerability on a social networking sites can be managed in three sequential steps: (1) identifying new ways in which a user can be vulnerable, (2) quantifying or measuring a user's vulnerability, and (3) reducing or mitigating them.[36]

Publication venues

Social media mining research articles are published in computer science, social science, and data mining conferences and journals:

Conferences

Conference papers can be found in proceedings of Knowledge Discovery and Data Mining (KDD), World Wide Web (WWW), Association for Computational Linguistics (ACL), Conference on Information and Knowledge Management (CIKM), International Conference on Data Mining (ICDM), Internet Measuring Conference (IMC).

Journals

Social media mining is also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases.

See also

Methods
Application domains
Related topics

References

  1. ^ a b c d e f g Zafarani, Reza; Abbasi, Mohammad Ali; Liu, Huan (2014). "Social Media Mining: An Introduction". Retrieved 15 November 2014.
  2. ^ Kaplan, Andreas M.; Haenlein, Michael (2010). "Users of the world, unite! The challenges and opportunities of social media". Business Horizons. {{cite web}}: Missing or empty |url= (help)
  3. ^ Tang, Jiliang; Tang, Jie; Liu, Huan (2014). "Recommendation in Social Media - Recent Advances and New Frontiers". In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
  4. ^ Tang, Jiliang; Hu, Xia; Liu, Huan (2013). "Social Recommendation: A Review" (PDF). Social Network Analysis and Mining.
  5. ^ Horowitz, Damon; Kamvar, Sepandar (2013). "The Anatomy of a Large-Scale Social Search Engine" (PDF). In Proceedings of the 19th international conference on World wide web, pp. 431-440. ACM, 2010.
  6. ^ Hu, Xia; Tang, Lei; Tang, Jiliang; Liu, Huan (2013). "Exploiting Social Relations for Sentiment Analysis in Microblogging" (PDF). In Proceedings of the 6th ACM International Conference on Web Search and Data Mining.
  7. ^ Hu, Xia; Tang, Jiliang; Gao, Huiji; Liu, Huan (2013). "Unsupervised Sentiment Analysis with Emotional Signals" (PDF). In Proceedings of the 22nd International World Wide Web Conference.
  8. ^ Hu, Xia; Tang, Jiliang; Zhang, Yanchao; Liu, Huan (2013). "Social Spammer Detection in Microblogging" (PDF). In Proceedings of the 23rd International Joint Conference on Artificial Intelligence.
  9. ^ Hu, Xia; Tang, Jiliang; Liu, Huan (2014). "Online Social Spammer Detection" (PDF). In Proceedings of the 28th AAAI Conference on Artificial Intelligence.
  10. ^ Hu, Xia; Tang, Jiliang; Liu, Huan (2014). "Leveraging Knowledge across Media for Spammer Detection in Microblogging" (PDF). In Proceedings of the 37th Annual ACM SIGIR Conference.
  11. ^ Hu, Xia; Tang, Jiliang; Gao, Huiji; Liu, Huan (2014). "Social Spammer Detection with Sentiment Information" (PDF). In Proceedings of the IEEE International Conference on Data Mining.
  12. ^ Tang, Jiliang; Liu, Huan (2012). "Feature Selection with Linked Data in Social Media" (PDF). In Proceedings of SIAM International Conference on Data Mining.
  13. ^ Tang, Jiliang; Liu, Huan (2014). "Feature Selection for Social Media Data" (PDF). ACM Transactions on Knowledge Discovery from Data (TKDD),8(4) Pages 19:1-19:27.
  14. ^ Tang, Jiliang; Liu, Huan (2012). "Unsupervised Feature Selection for Linked Social Media Data" (PDF). In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  15. ^ Tang, Jiliang; Liu, Huan (2014). "Unsupervised Feature Selection for Linked Social Media Data" (PDF). IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(12): 2914-1927.
  16. ^ Tang, Jiliang; Liu, Huan (2014). "Trust in Social Computing". In Proceedings of the 23rd International World Wide Web Conference.
  17. ^ Tang, Jiliang; Gao, Huiji; Liu, Huan (2012). "mTrust: Discerning Multi-Faceted Trust in a Connected World" (PDF). the 5th ACM International Conference on Web Search and Data Mining.
  18. ^ Tang, Jiliang; Gao, Huiji; DasSarma, Atish; Liu, Huan (2012). "eTrust: Understanding Trust Evolution in an Online World" (PDF). In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  19. ^ Tang, Jiliang; Gao, Huiji; Hu, Xia; Liu, Huan (2013). "Exploiting Homophily Effect for Trust Prediction" (PDF). the 6th ACM International Conference on Web Search and Data Mining.
  20. ^ Tang, Jiliang; Hu, Xia; Liu, Huan (2014). "Is Distrust the Negation of Trust? The Value of Distrust in Social Media" (PDF). In Proceedings of ACM Hypertext conference.
  21. ^ Tang, Jiliang; Hu, Xia; Chang, Yi; Liu, Huan (2014). "Predictability of Distrust with Interaction Data" (PDF). ACM International Conference on Information and Knowledge Management.
  22. ^ Tang, Jiliang; Chang, Shiyu; Aggarwal, Charu; Liu, Huan (2015). "Negative Link Prediction in Social Media" (PDF). In Proceedings ofACM International Conference on Web Search and Data Mining.
  23. ^ Bruno, Nicola (2011). "Tweet first, verify later? How real-time information is changing the coverage of worldwide crisis events". Oxford: Reuters Institute for the Study of Journalism, University of Oxford. 10: 2010–2011. {{cite journal}}: |access-date= requires |url= (help)
  24. ^ Sakaki, Takashi; Okazaki, Makoto; Yutaka, Matsuo (2010). "Earthquake shakes Twitter users: real-time event detection by social sensors". Proceedings of the 19th international conference on World wide web: 851–860. {{cite journal}}: |access-date= requires |url= (help)
  25. ^ Mendoza, Marcelo; Poblete, Barbara; Castillo, Carlos (2010). "Twitter under crisis: Can we trust what we RT?". Proceedings of the first workshop on social media analytics: 71–79. {{cite journal}}: |access-date= requires |url= (help)
  26. ^ Kumar, Shamanth; Barbier, Geoffrey; Abbasi, Mohammad Ali; Liu, Huan (2011). "TweetTracker: An Analysis Tool for Humanitarian and Disaster Relief". The 5th International AAAI Conference on Weblogs and Social Media. Retrieved 1 December 2014.
  27. ^ Kumar, Shamanth; Hu, Xia; Liu, Huan (2014). "A behavior analytics approach to identifying tweets from crisis regions". Proceedings of the 25th ACM conference on Hypertext and social media: 255–260.
  28. ^ Gao, Huiji; Tang, Jiliang; Liu, Huan (2012). "Exploring Social-Historical Ties on Location-Based Social Networks" (PDF). In Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media.
  29. ^ Gao, Huiji; Tang, Jiliang; Liu, Huan (2012). "Mobile Location Prediction in Spatio-Temporal Context" (PDF). Nokia Mobile Data Challenge Workshop 2012.
  30. ^ Gao, Huiji; Tang, Jiliang; Liu, Huan (2012). "gSCorr: Modeling Geo-Social Correlations for New Check-ins on Location-Based Social Networks" (PDF). In Proceedings of the 21st ACM International Conference on Information and Knowledge Management.
  31. ^ Gao, Huiji; Tang, Jiliang; Hu, Xia; Liu, Huan (2013). "Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks" (PDF). In Proceedings of the 7th ACM Recommender Systems Conference.
  32. ^ Gao, Huiji; Tang, Jiliang; Hu, Xia; Liu, Huan (2014). "Content-Aware Point of Interest Recommendation on Location-Based Social Networks" (PDF). In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.
  33. ^ Gao, Huiji; Tang, Jiliang; Liu, Huan (2014). "Personalized Location Recommendation on Location-based Social Networks" (PDF). In Proceedings of the 8th ACM Recommender Systems Conference.
  34. ^ Barbier, Geoffrey; Feng, Zhuo; Gundecha, Pritam; Liu, Huan (2013). "Provenance Data in Social Media". Synthesis Lectures on Data Mining and Knowledge Discovery.
  35. ^ Gundecha, Pritam; Feng, Zhuo; Liu, Huan (2013). "Seeking Provenance of Information in Social Media" (PDF). In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management Conference.
  36. ^ Gundecha, Pritam; Barbier, Geoffrey; Tang, Jiliang; Liu, Huan (2014). "User Vulnerability and its Reduction on a Social Networking Site" (PDF). Journals of Transactions on Knowledge Discovery from Data.
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