Assessing topic discovery evaluation measures on Facebook publications of political activists in Brazil
Pages 25 - 32
Abstract
Automatic topic detection in document collections is an important tool for various tasks. In particular, it is valuable for studying and understanding socio-political phenomena. A currently relevant example is the automatic analysis of streams of posts issued by different activist groups in the current Brazilian turmoil, through the analysis of the generated streams of texts published on the web. It is useful to determine the relative importance of the different topics identified. We can find in the literature proposals for measuring topic relevance. In this paper, we adopt two of such measures and apply them to data sets extracted from Facebook pages related to Brazilian political activism. On top of the analysis, we then carry an experimental evaluation of the human interpretability for these two measures by comparing their outcomes with the opinion of three Brazilian professionals from the field of Communication Science and media-activists.
References
[1]
Nikolaos Aletras and Mark Stevenson."Evaluating Topic Coherence Using Distributional Semantics". In: Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013). 2013.
[2]
Loulwah Alsumait et al. "Topic Significance Ranking of LDA Generative Models". In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I. ECML PKDD '09. Bled, Slovenia: Springer-Verlag, 2009, pp. 67--82.
[3]
Anat Ben-David and Ariadna Matamoros Fernández. "Hate Speech and Covert Discrimination on Social Media: Monitoring the Facebook Pages of Extreme-Right Political Parties in Spain". In: International Journal of Communication 10 (2016).
[4]
W. Lance Bennett and Alexandra Segerberg. The Logic of Connective Action: Digital Media and the Personalization of Contentious Politics. Cambridge University Press, 2013.
[5]
Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python. 1st. O'Reilly Media, Inc., 2009.
[6]
David M. Blei. "Probabilistic Topic Models". In: Commun. ACM 55.4 (Apr. 2012), pp. 77--84.
[7]
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet Allocation". In: J. Mach. Learn. Res. 3 (Mar. 2003), pp. 993--1022.
[8]
Manuel Castells. Communication Power. 2009.
[9]
Manuel Castells. Redes de Indignacion y Esperanza. Alianza Editorial, 2012.
[10]
Jonathan Chang et al. "Reading Tea Leaves: How Humans Interpret Topic Models". In: Neural Information Processing Systems. Vancouver, British Columbia, 2009.
[11]
Jason Chuang, Christopher D. Manning, and Jeffrey Heer. "Termite: Visualization Techniques for Assessing Textual Topic Models". In: Advanced Visual Interfaces. 2012.
[12]
Javier Toret (coord.) Tecnopolítica y 15M: La potencia de las multitudes conectadas. Barcelona, 2013.
[13]
Ali Daud et al."Knowledge discovery through directed probabilistic topic models: a survey". In: Frontiers of Computer Science in China 4.2 (2010), pp. 280--301.
[14]
Yi Fang et al. "Mining Contrastive Opinions on Political Texts Using Cross-perspective Topic Model". In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. WSDM '12. Seattle, Washington, USA: ACM, 2012, pp. 63--72.
[15]
Toshiaki Funatsu et al."Extracting Representative Words of a Topic Determined by Latent Dirichlet Allocation". In: Proc. The Sixth International Conference on Information, Process, and Knowledge Management (eKNOW 2014). 2014.
[16]
Justin Grimmer. "A bayesian hierarchical topic model for political texts: Measuring expressed agendas in senate press releases". In: In Proceedings of the First Workshop on Social Media Analytics, SOMA '10. 2009.
[17]
Katrina Kimport Jennifer Earl. Digitally Enabled Social Change: Activism in the Internet Age. MIT Press, 2011.
[18]
Bruno Latour. "Beware, your imagination leaves digital traces". In: Times Higher Literary Supplemen (2007).
[19]
David Mimno et al. "Optimizing Semantic Coherence in Topic Models". In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. EMNLP '11. Edinburgh, United Kingdom: Association for Computational Linguistics, 2011, pp. 262--272.
[20]
Jaimie Murdock and Colin Allen. "Visualization Techniques for Topic Model Checking". In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI'15. Austin, Texas: AAAI Press, 2015, pp. 4284--4285.
[21]
David Newman et al. "Automatic Evaluation of Topic Coherence". In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. HLT '10. Los Angeles, California: Association for Computational Linguistics, 2010, pp. 100--108.
[22]
David Newman et al. "Evaluating Topic Models for Digital Libraries". In: Proceedings of the 10th Annual Joint Conference on Digital Libraries. JCDL '10. Gold Coast, Queensland, Australia: ACM, 2010, pp. 215--224.
[23]
David Newman et al. "Evaluating Topic Models for Digital Libraries". In: Proceedings of the 10th Annual Joint Conference on Digital Libraries. JCDL '10. Gold Coast, Queensland, Australia: ACM, 2010, pp. 215--224.
[24]
Quentin Pleplé. "Interactive Topic Modeling". PhD thesis. University of California, San Diego, 2013.
[25]
Bernhard Rieder. "Studying Facebook via Data Extraction: The Netvizz Application". In: Proceedings of the 5th Annual ACM Web Science Conference. WebSci '13. Paris, France: ACM, 2013, pp. 346--355.
[26]
Margaret E. Roberts et al."The Structural Topic Model and Applied Social Science". In: Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation. Peer-Reviewed Conference Workshop. Selected for Oral Presentation. Lake Tahoe, Nevada, 2013.
[27]
Carson Sievert and Kenneth E. Shirley. "LDAvis: A method for visualizing and interpreting topics". In: ACL Workshop on Interactive Language Learning, Visualization, and Interfaces. Baltimore, 2014.
[28]
Hanna M. Wallach et al. "Evaluation Methods for Topic Models". In: Proceedings of the 26th Annual International Conference on Machine Learning. ICML '09. Montreal, Quebec, Canada: ACM, 2009, pp. 1105--1112.
[29]
William Warner and Julia Hirschberg. "Detecting Hate Speech on the World Wide Web". In: Proceedings of the Second Workshop on Language in Social Media. LSM '12. Montreal, Canada: Association for Computational Linguistics, 2012, pp. 19--26.
[30]
Wayne Xin Zhao et al. "Comparing Twitter and Traditional Media Using Topic Models". In: Proceedings of the 33rd European Conference on Advances in Information Retrieval. ECIR'11. Dublin, Ireland: Springer-Verlag, 2011, pp. 338--349.
Index Terms
- Assessing topic discovery evaluation measures on Facebook publications of political activists in Brazil
Recommendations
Hidden Topic Sentiment Model
WWW '16: Proceedings of the 25th International Conference on World Wide WebVarious topic models have been developed for sentiment analysis tasks. But the simple topic-sentiment mixture assumption prohibits them from finding fine-grained dependency between topical aspects and sentiments. In this paper, we build a Hidden Topic ...
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 ManagementAspect-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 ...
The dual-sparse topic model: mining focused topics and focused terms in short text
WWW '14: Proceedings of the 23rd international conference on World wide webTopic modeling has been proved to be an effective method for exploratory text mining. It is a common assumption of most topic models that a document is generated from a mixture of topics. In real-world scenarios, individual documents usually concentrate ...
Comments
Information & Contributors
Information
Published In
July 2016
152 pages
ISBN:9781450340755
DOI:10.1145/2948992
- Editor:
- Evan Desai,
- General Chair:
- Bipin C. Desai,
- Program Chairs:
- Ana Alameida,
- Jorge Bernardino
Copyright © 2016 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]
In-Cooperation
- BytePress
- ISEP
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 July 2016
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
C3S2E '16
C3S2E '16: Ninth International C* Conference on Computer Science & Software Engineering
July 20 - 22, 2016
Porto, Portugal
Acceptance Rates
Overall Acceptance Rate 12 of 42 submissions, 29%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 166Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Sep 2024
Other Metrics
Citations
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.
Sign in