research-article

Is That Twitter Hashtag Worth Reading

Authors:

Sanjay SinghAuthors Info & Claims

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Pages 272 - 277

https://doi.org/10.1145/2791405.2791526

Published: 10 August 2015 Publication History

Abstract

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.

References

[1]

O. Alonso, C. Carson, D. Gerster, X. Ji, and S. U. Nabar. Detecting uninteresting content in text streams. In Proceedings of SIGIR 2010 Workshop on Crowdsourcing for Search Evaluation (CSE 2010), pages 39--42, Geneva, Switzerland, July 2010.

[2]

M. G. Armentano, D. Godoy, and A. A. Amandi. Followee recommendation based on text analysis of micro-blogging activity. Inf. Syst., 38(8):1116--1127, Nov 2013.

Digital Library

[3]

BBC. Deadly tsunami strkes in pacific. news.bbc.co.uk/2/hi/asia-pacific/8281616.stm, 2009.

[4]

BBC. Nepal earthquake: Eight million people affected. news.bbc.co.uk/news/worls-asia-32492232, 2015.

[5]

D. M. Blei. Probabilistic topic models. Commun. ACM, 55(4):77--84, April 2012.

Digital Library

[6]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, Mar. 2003.

[7]

C. Castillo, M. Mendoza, and B. Poblete. Information credibility on twitter. In Proceedings of the 20th International Conference on World Wide Web, WWW '11, pages 675--684, New York, USA, 2011. ACM.

Digital Library

[8]

J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi. Short and tweet: Experiments on recommending content from information streams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10, pages 1185--1194, New York, NY, USA, 2010. ACM.

Digital Library

[9]

J. Duan and J. Zeng. Web objectionable text content detection using topic modeling technique. Expert Syst. Appl., 40(15):6094--6104, Nov 2013.

Digital Library

[10]

Y. Duan, L. Jiang, T. Qin, M. Zhou, and H.-Y. Shum. An empirical study on learning to rank of tweets. In Proceedings of the 23rd International Conference on Computational Linguistics, COLING '10, pages 295--303, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.

Digital Library

[11]

T. S. N. L. P. Group. Standford topic modeling toolbox. {Available Online} nlp.stanford.edu/software/tmt/tmt-0.4/, 2010.

[12]

K. K. H. W. Lauw, A. Ntoulas. Estimating the quality of postings in the real-time web. In Proceedings of WSDM 2010 Workshop on search in social media SSM'10, pages 1--4, Feb 2010.

[13]

S. Jain. 40 most popular social networking sites of the world. Social Media Today-Blog, 2012.

[14]

N. Kawamae. Trend analysis model: Trend consists of temporal words, topics, and timestamps. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM '11, pages 317--326, New York, NY, USA, 2011. ACM.

Digital Library

[15]

H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web, WWW '10, pages 591--600, New York, USA, 2010. ACM.

Digital Library

[16]

C. Lin and Y. He. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM '09, pages 375--384, New York, NY, USA, 2009. ACM.

Digital Library

[17]

M. Pennacchiotti and S. Gurumurthy. Investigating topic models for social media user recommendation. In Proceedings of the 20th International Conference Companion on World Wide Web, WWW '11, pages 101--102, New York, NY, USA, 2011. ACM.

Digital Library

[18]

N. Project. Nltk 3.0 documentation. {Available Online} www.nltk.org/data.html, 2014.

[19]

D. Ramage, S. T. Dumais, and D. J. Liebling. Characterizing microblogs with topic models. In W. W. Cohen and S. Gosling, editors, ICWSM. The AAAI Press, 2010.

[20]

D. M. Romero, W. Galuba, S. Asur, and B. A. Huberman. Influence and passivity in social media. In Proceedings of the 20th International Conference Companion on World Wide Web, WWW '11, pages 113--114, New York, NY, USA, 2011. ACM.

Digital Library

[21]

Twitter. Rest apis. {Available Online} https://dev.twitter.com/rest/public, 2014.

[22]

Twitter. Using hashtags on twitter. https://support.twitter.com/articles/49309-using-hashtags-on-twitter, 2015. {Online; accessed 10-April-2015}.

[23]

X. Wang and A. McCallum. Topics over time: A non-markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '06, pages 424--433, New York, NY, USA, 2006. ACM.

Digital Library

[24]

Wikipedia. Twitter revolution. en.wikipedia.org/wiki/Twitter_Revolution, 2014.

[25]

Wikipedia. Latent dirichlet allocation --- wikipedia, the free encyclopedia, 2015. {Online; accessed 28-April-2015}.

[26]

Wikipedia. Sentiment analysis --- wikipedia, the free encyclopedia. http://en.wikipedia.org/w/index.php?title=Sentiment_analysis&oldid=654779025, 2015. {Online; accessed 15-April-2015}.

[27]

Wikipedia. Topic model --- wikipedia, the free encyclopedia, 2015. {Online; accessed 28-April-2015}.

[28]

M.-C. Yang and H.-C. Rim. Identifying interesting twitter contents using topical analysis. Expert Systems with Applications, 41(9):4330--4336, 2014.

Digital Library

[29]

W. X. Zhao, J. Jiang, J. Weng, J. He, E.-P. Lim, H. Yan, and X. Li. Comparing twitter and traditional media using topic models. In Proceedings of the 33rd European Conference on Advances in Information Retrieval, ECIR'11, pages 338--349, Berlin, Heidelberg, 2011. Springer-Verlag.

Digital Library

Cited By

Litvinova OMatin FMatin MZima-Kulisiewicz BTomasik CSiddiquea BStoyanov JAtanasov AWillschke H(2023)Patient safety discourse in a pandemic: a Twitter hashtag analysis study on #PatientSafetyFrontiers in Public Health10.3389/fpubh.2023.126873011Online publication date: 16-Nov-2023
https://doi.org/10.3389/fpubh.2023.1268730
Amitani RMatsumoto KYoshida MKita K(2021)Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task LearningApplied Sciences10.3390/app11221056711:22(10567)Online publication date: 10-Nov-2021
https://doi.org/10.3390/app112210567
Dang MSingh S(2016)Context based interesting tweet recommendation framework2016 IEEE Annual India Conference (INDICON)10.1109/INDICON.2016.7838957(1-5)Online publication date: Dec-2016
https://doi.org/10.1109/INDICON.2016.7838957
Show More Cited By

Index Terms

Is That Twitter Hashtag Worth Reading
1. Information systems
  1. Information retrieval
  2. Information storage systems

Recommendations

Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
WWW '17: Proceedings of the 26th International Conference on World Wide Web

Hashtags have become a powerful tool in social platforms such as Twitter to categorize and search for content, and to spread short messages across members of the social network. In this paper, we study temporal hashtag usage practices in Twitter with ...
Hashtag homophily in twitter network: Examining a controversial cause-related marketing campaign
Abstract
Social media such as Twitter generate vibrant discussions related to key sociopolitical issues and have great ability to project various discourses into public arena. Yet, these discourses can be overwhelming and heated, in particular ...
Highlights
- Twitter users are very reactive to influencers and other users when discussing Gillette’s cause-related marketing campaign.
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

August 2015

763 pages

ISBN:9781450333610

DOI:10.1145/2791405

Editor:
Indu Nair
SCMS, Kochi, India
,
General Chairs:
Sushmita Mitra
Indian Statistical Institute, Kolkata, India
,
Ljiljana Trajković
Simon Fraser University, Canada
,
Program Chairs:
Punam Bedi
University of Delhi, India
,
Suzanne McIntosh
New York University and Cloudera Inc., USA
,
M. S. Rajasree
IIITM-K, Trivandrum, India

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WCI '15

WCI '15: Third International Symposium on Women in Computing and Informatics

August 10 - 13, 2015

Kochi, India

Acceptance Rates

WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;

Overall Acceptance Rate 98 of 452 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
177
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Litvinova OMatin FMatin MZima-Kulisiewicz BTomasik CSiddiquea BStoyanov JAtanasov AWillschke H(2023)Patient safety discourse in a pandemic: a Twitter hashtag analysis study on #PatientSafetyFrontiers in Public Health10.3389/fpubh.2023.126873011Online publication date: 16-Nov-2023
https://doi.org/10.3389/fpubh.2023.1268730
Amitani RMatsumoto KYoshida MKita K(2021)Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task LearningApplied Sciences10.3390/app11221056711:22(10567)Online publication date: 10-Nov-2021
https://doi.org/10.3390/app112210567
Dang MSingh S(2016)Context based interesting tweet recommendation framework2016 IEEE Annual India Conference (INDICON)10.1109/INDICON.2016.7838957(1-5)Online publication date: Dec-2016
https://doi.org/10.1109/INDICON.2016.7838957
Mathur VStavrakas YSingh S(2016)Intelligence analysis of Tay Twitter bot2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I.2016.7917966(231-236)Online publication date: Dec-2016
https://doi.org/10.1109/IC3I.2016.7917966

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents