Sage Journals: Discover world-class research

Abstract

The microblogging service Twitter has witnessed a rapid increase in its adopters ever since it’s discovery in October 2006. Today it has become a medium of communication as well as spread of information. Hashtags are created in twitter by users whenever an event of significant importance occurs and hence they become trending on twitter network. Once hashtags are created on twitter platform, the tweeters may communicate within a particular community of interest following and tweeting to any particular hashtag conversations. In this paper, we propose the design of Community based Hashtag Recommender System (CHRS) for twitter users. This will help the users by expanding their hashtag base and hence strengthening the hashtag conversation for a particular event. The tweets collected over a period of time for some particular hashtags have been categorised to communities based on sentiment analysis of the tweets. Once the process of community detection completes, the existing users are found in the tweets. Further the idea of Hashtag frequency- Inverse Community Frequency (HF-ICF) has been suggested and deployed to find hashtags which uniquely distinguish the users found earlier. Finally relevance score is computed based on the idea of collaborative filtering approach to recommendation, for various hashtags used by the users. A prototype of the system is developed using the statistical tool R and experimental analysis has been carried out. Tweets of national concern in India pertaining to ‘demonetisation’ have been collected and used for experimental purposes.

Keywords

Recommender system demonetisation twitter hashtag community detection HF-ICF

Get full access to this article

View all access options for this article.

References

Banker

, MongoDB in action.Greenwich, CT, USA: Manning Publications Co.,2011.

Bedi

, Gautam

, and Sharma

, Richa and , Using novelty score of unseen items to handle popularity bias in recommender systems, in Contemporary Computing and Informatics (IC3I), 2014 International Conference on, 2014, pp. 934–939.

Bedi

and Sharma

, Community detection in social networks, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery6 (2016), 115–135.

Bedi

, Sharma

, Vashisth

, Goel

and Dhanda

, Handling cold start problem in Recommender Systems by using Interaction Based Social Proximity factor, in Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on, 2015, pp.1987–1993.

Bedi

and Vashisth

, Empowering recommender systems using trust and argumentation, Information Sciences279 (2014), 569–586.

Bruns

and Burgess

J.E.

, The use of Twitter hashtags in the formation of ad hoc publics, in Proceedings of the 6th European Consortium for Political Research (ECPR) General Conference 2011, University of Iceland, Reykjavik, 2011.

Bruns

and Stieglitz

, Metrics for Understanding Communication on Twitter, in Twitter and society. 89, ed New York: Peter Lang, 2014, pp. 69–82.

Chen

, Nairn

, Nelson

, Bernstein

and Chi

, Short and tweet: Experiments on recommending content from information streams, inProceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, Georgia, USA, 2010, pp. 1185–1194.

Elmongui

H.G.

, Mansour

, Morsy

, Khater

, El-Sharkasy

and Ibrahim

, TRUPI: Twitter Recommendation Based on Users’ Personal Interests, in Computational Linguistics and Intelligent Text Processing, 16th International Conference, CICLing 2015, Proceedings Part IIvol. 9042, ed: Springer, 2015, pp. 272–284.

10.

Fortunato

, Community detection in graphs, Physics Reports486 (2010), 75–174.

11.

Hahsler

, Recommenderlab: A Framework for Developing and Testing Recommendation Algorithms, Southern Methodist University, 2011.

12.

Hannon

, Bennett

and Smyth

, Recommending twitter users to follow using content and collaborative filtering approaches, in Proceedings of the fourth ACM Conference on Recommender Systems, RecSys10, Barcelona, Spain, 2010, pp. 199–206.

13.

Herlocker

J.L.

, Konstan

J.A.

, Terveen

L.G.

and Riedl

J.T.

, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), 22, 2004, pp. 5–53.

14.

Jannach

, Zanker

, Felfernig

and Friedrich

, Recommender Systems: An Introduction. New York, USA:Cambridge University Press2010.

15.

Kristine

and Murray

R.W.

, Social networking goes abroad, International Educator16 (2007), 56–59.

16.

Kumar

and Sebastian

T.M.

, Sentiment analysis on twitter, IJCSI International Journal of Computer Science Issues9 (2012), 372–378.

17.

Kumar

and Teeja

M.S.

, Sentiment analysis: A perspective on its past, present and future, International Journal of Intelligent Systems and Applications4 (2012), 1–14.

18.

Kywe

S.M.

, Hoang

T.-A.

, Lim

E.-P.

and Zhu

, On recommending hashtags in twitter networks, in International Conference on Social Informatics, 2012, pp.337–350.

19.

Kywe

S.M.

, Hoang

T.-A.

, Lim

E.-P.

and Zhu

, On recommending hashtags in twitter networks, in Social Informatics, 4th International Conference, SocInfo 2012 Proceedings 7710, ed: Springer, 2012, pp. 337–350.

20.

Manning

C.D.

, Surdeanu

, Bauer

, Finkel

J.R.

, Bethard

and McClosky

, The stanford corenlp natural language processing toolkit, in ACL (System Demonstrations), 2014, pp. 55–60.

21.

Otsuka

, Wallace

S.A.

and Chiu

, Design and evaluation of a twitter hashtag recommendation system, in Proceedings of the 18th International Database Engineering & Applications Symposium, 2014, pp. 330–333.

22.

Otsuka

, Wallace

S.A.

and Chiu

, A hashtag recommendation system for twitter data streams, Computational Social Networks3 (2016), 1–26.

23.

Ramos

, Using tf-idf to determine word relevance in document queries, in Proceedings of the first Instructional Conference on Machine Learning, New Brunswick: NJ, USA, Rutgers University, 2003.

24.

Ricci

, Rokach

and Shapira

, Introduction to recommender systems handbook, Springer, USA, 2011.

25.

Tang

and Liu

, Community Detection and Mining in Social Media, Synthesis Lectures on Data Mining and Knowlegde Discovery: Morgan and Claypool, 2010.

26.

Zangerle

, Gassler

and Specht

, On the impact of text similarity functions on hashtag recommendations in microblogging environments, Social Network Analysis and Mining3 (2013), 889–898.

27.

Zangerle

, Gassler

and Specht

, Recommending#-tags in twitter, in Proceedings of the Workshop on Semantic Adaptive Social Web (SASWeb 2011), 2011, pp.67–78.