Classifying Content Quality and Interaction Quality on Online Social Networks

  • Amtul WaheedEmail author
  • Jana Shafi
  • P. Venkata Krishna
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Today’s OSN puts web forums, QA communities and blogging site all together on global stand. The drastic revolution in the world of online social networking sites and increasing number of users and time spent on OSN express a concern for user generated content and quality of interaction. By analysing user generated content and user interaction on OSN we explore how content quality and interaction quality impacts on dynamic online social system. In this paper we show how content quality and interaction quality measured between different users on OSN portals.


  1. 1.
    Adamic LA, Zhang J, Bakshy E, Ackerman MS (2008) Knowledge sharing and yahoo answers: everyone knows something. In: WWW’08: Proceedings of 17th international conference on World Wide Web. ACM, New York, pp 665–674Google Scholar
  2. 2.
    Agichtein E, Castillo C, Donato D (2008) Aristides Gionis, and Gilad Mishne. Finding high-quality content in social media. In: WSDM’08: Proceedings of international conference on web search and web data mining. ACM, New York, pp 183–194 Google Scholar
  3. 3.
    Bian J, Liu Y, Agichtein E, Zha H (2008) Finding the right facts in the crowd: factoid question answering over social media. In: Proceedings of 17th international conference on World Wide Web. ACM, pp 467–476Google Scholar
  4. 4.
    Bian J, Liu Y, Zhou D, Agichtein E, Zha H (2009) Learning to recognize reliable users and content in social media with coupled mutual reinforcement. In: WWW’09: Proceedings of 18th international conference on World Wide Web. ACM, New York, pp 51–60Google Scholar
  5. 5.
    Harper FM, Moy D, Konstan JA (2009) Facts or friends? Distinguishing informational and conversational questions in social Q&A sites. In: Proceedings of 27th international conference on human factors in computing systems. ACM, pp 759–768Google Scholar
  6. 6.
    Liu Y, Bian J, Agichtein E (2008) Predicting information seeker satisfaction in community question answering. In: Proceedings 31st annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 483–490Google Scholar
  7. 7.
    Sun K, Cao Y, Song X, Song Y-I, Wang X, Lin C-Y (2009) Learning to recommend questions based on user ratings. In: Proceedings of 18th ACM conference on information and knowledge management, CIKM’09. ACM, New York, pp 751–758Google Scholar
  8. 8.
    Sang-Hun C (2007) To outdo Google, Naver taps into Korea’s collective wisdom. International Herald Tribune, 4 July 2007Google Scholar
  9. 9.
    Anderson C (2006) The long tail: why the future of business is selling less of more. HyperionGoogle Scholar
  10. 10.
    Guo K, Bhakta P, Narayen S, Loke ZK (2012) Predicting human compatibility in online chat networks. Unpublished manuscript, Department of Computer Science, Stanford University, Stanford, CaliforniaGoogle Scholar
  11. 11.
    Page L, Brin S, Motwani R, Winograd T (1998) The PageRank citation ranking: bringing order to the Web. Technical report, Stanford Digital Library Technologies ProjectGoogle Scholar
  12. 12.
    Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632MathSciNetCrossRefGoogle Scholar
  13. 13.
    Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagation of trust and distrust. In: WWW ‘04: Proceedings of the 13th international conference on World Wide Web. ACM Press, New York, pp 403–412Google Scholar
  14. 14.
    Su Q, Pavlov D, Chow J-H, Baker WC (2007) Internet-scale collection of human-reviewed data. In: WWW ‘07: Proceedings of the 16th international conference on World Wide Web. ACM Press, New York, pp 231–240Google Scholar
  15. 15.
    Zhang J, Ackerman MS, Adamic L (2007) Expertise networks in online communities: structure and algorithms. In WWW ‘07: Proceedings of the 16th international conference on world wide web. ACM Press, New York, pp 221–230Google Scholar
  16. 16.
    Burstein J, Wolska M (2003) Toward evaluation of writing style: finding overly repetitive word use in student essays. In: EACL ‘03: Proceedings of the tenth conference on European chapter of the Association for computational linguistics, Morristown, NJ. Association for Computational Linguistics, pp 35–42Google Scholar
  17. 17.
    Agichtein E, Brill E, Dumais ST, Ragno R (2006) Learning user interaction models for predicting web search result preferences. In: SIGIR, pp 3–10Google Scholar
  18. 18.
    Baeza-Yates R, Tiberi A (2007) Extracting semantic relations from query logs. In: Proceedings of 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 76–85Google Scholar
  19. 19.
    Welser HT, Gleave E, Fisher D, Smith M (2007) Visualizing the signatures of social roles in online discussion groups. J Soc Struct 8(2):1–32Google Scholar
  20. 20.
    Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniquesGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer SciencePrince Sattam Bin Abdul Aziz UniversityRiyadhIndia
  2. 2.Department of Computer ScienceSri Padmavati Mahila VisvavidyalayamTirupatiIndia

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