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Short-text learning in social media: a review

Published online by Cambridge University Press:  06 June 2019

Antonela Tommasel  and
Daniela Godoy
Antonela Tommasel*
Affiliation:
ISISTAN, UNICEN-CONICET, Campus Universitario, Tandil (B7001BBO), Argentina; e-mail: [email protected], [email protected]
Daniela Godoy
Affiliation:
ISISTAN, UNICEN-CONICET, Campus Universitario, Tandil (B7001BBO), Argentina; e-mail: [email protected], [email protected]
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Abstract

Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.

Type
Review
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e7
Copyright
© Cambridge University Press 2019 

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References

Aggarwal, C. C. 2014. A survey of stream classification algorithms. In Data Classification: Algorithms and Applications, Aggarwal, C. C. (ed). CRC Press, 245–274.CrossRefGoogle Scholar
Aggarwal, C. C. & Zhai, C. X. 2012. A survey of text classification algorithms. In Mining Text Data, Aggarwal, C. C. & Zhai, C. X. (eds). Springer US, 163–222. ISBN 978-1-4614-3222-7.Google Scholar
Alelyani, S., Tang, J. & Liu, H. 2013. Feature selection for clustering: a review. In Data Clustering: Algorithms and Applications. Chapman and Hall/CRC, 29–60.Google Scholar
Arthur, D. & Vassilvitskii, S. 2007. k-means++: the advantages of careful seeding. In SODA ‘07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA. ISBN 978-0-898716-24-5.Google Scholar
Asur, S. & Huberman, B. A. 2010. Predicting the future with social media. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 1, 492–499.CrossRefGoogle Scholar
Becker, H., Naaman, M. & Gravano, L. 2011. Beyond trending topics: real-world event identification on Twitter. In Fifth International AAAI Conference on Weblogs and Social Media.Google Scholar
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.CrossRefGoogle Scholar
Broder, A. Z. 1997. On the resemblance and containment of documents. In Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), 21–29.Google Scholar
Carullo, M., Binaghi, E. & Gallo, I. 2009. An online document clustering technique for short web contents. Pattern Recognition Letters 30(10), 870–876.CrossRefGoogle Scholar
Ciampaglia, G. L., Shiralkar, P., Rocha, L. M., Bollen, J., Menczer, F. & Flammini, A. 2015. Computational fact checking from knowledge networks. PLOS ONE 10(6), 1–13.Google ScholarPubMed
Collins, R., May, D., Weinthal, N. & Wicentowski, R. 2015. SWAT-CMW: classification of Twitter emotional polarity using a multiple-classifier decision schema and enhanced emotion tagging. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 669–672. Association for Computational Linguistics.CrossRefGoogle Scholar
Croft, W. B., Metzler, D. & Strohman, T. 2010. Search Engines: Information Retrieval in Practice, 283. Addison- Wesley Reading.Google Scholar
Cui, R., Agrawal, G., Ramnath, R. & Khuc, V. 2016. Ensemble of heterogeneous classifiers for improving automated tweet classification. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 1045–1052.CrossRefGoogle Scholar
Dai, X., Bikdash, M. & Meyer, B. 2017. From social media to public health surveillance: word embedding based clustering method for Twitter classification. In SoutheastCon 2017, 1–7.Google Scholar
de la Rosa, G. R., Montes-y-Gémez, M. Solorio, T. & Pineda, L. V. 2013. A document is known by the company it keeps: neighborhood consensus for short text categorization. Language Resources and Evaluation 47(1), 127–149.Google Scholar
Deutsch, P. 1996. DEFLATE Compressed Data Format Specification version 1.3. RFC 1951 (Informational).Google Scholar
Dietterich, T. G. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems. Springer, 1–15. ISBN 978-3-540-45014-6.Google Scholar
Efron, M., Lin, J., He, J. & de Vries, A. 2014. Temporal feedback for tweet search with non-parametric density estimation. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14, 33–42. ACM. ISBN 978-1-4503-2257-7.Google Scholar
Ferrara, E., JafariAsbagh, M., Varol, O., Qazvinian, V., Menczer, F. & Flammini, A. 2013. Clustering memes in social media. In Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).Google Scholar
Forman, G. 2004. A pitfall and solution in multi-class feature selection for text classification. In ICML, Brodley, C. E. (ed), ACM International Conference Proceeding Series, 69. ACM.Google Scholar
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M. & Bouchachia, A. 2014. A survey on concept drift adaptation. ACM Computing Surveys 46(4), 44:1–44:37. ISSN 0360-0300.Google Scholar
Gandomi, A. & Haider, M. 2015. Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management 35(2), 137–144. ISSN 0268-4012.CrossRefGoogle Scholar
Guyon, I. & Elisseeff, A. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182.Google Scholar
Hu, M. & Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, 168–177. ACM. ISBN 1-58113-888-1.Google Scholar
Irfan, R., King, C. K., Grages, D., Ewen, S., Khan, S. U., Madani, S. A., Kolodziej, J., Wang, L., Chen, D. & Rayes, A. 2015. A survey on text mining in social networks. The Knowledge Engineering Review 30(2), 157–170.CrossRefGoogle Scholar
Iwata, T., Watanabe, S., Yamada, T. & Ueda, N. 2009. Topic tracking model for analyzing consumer purchase behavior. In IJCAI, 9, 1427–1432.Google Scholar
Jain, A. K. & Dubes, R. C. 1988. Algorithms for Clustering Data. Prentice-Hall, Inc. ISBN 0-13-022278-X.Google Scholar
Jia, C., Carson, M. B., Wang, X. & Yu, J. 2018. Concept decompositions for short text clustering by identifying word communities. Pattern Recognition 76, 691–703. ISSN 0031-3203.Google Scholar
Kang, J. H., Lerman, K. & Plangprasopchok, A. 2010. Analyzing microblogs with affinity propagation. In Proceedings of the First Workshop on Social Media Analytics, 67–70. ACM.CrossRefGoogle Scholar
Khan, F. H., Bashir, S. & Qamar, U. 2014. Tom: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems 57, 245–257. ISSN 0167-9236.Google Scholar
Kim, K., Chung, B.-S., Choi, Y., Lee, S., Jung, J.-Y. & Park, J. 2014. Language independent semantic kernels for short-text classification. Expert Systems with Applications 41(2), 735–743. ISSN 0957-4174.CrossRefGoogle Scholar
Kim, S., Jeon, S., Kim, J., Park, Y.-H. & Yu, H. 2012. Finding core topics: topic extraction with clustering on tweet. In 2012 Second International Conference on Cloud and Green Computing (CGC), 777–782.CrossRefGoogle Scholar
Kim, Y.-H., Seo, S., Ha, Y.-H., Lim, S. & Yoon, Y. 2013. Two applications of clustering techniques to Twitter: community detection and issue extraction. Discrete Dynamics in Nature and Society 2013.Google Scholar
Li, C., Sun, A. & Datta, A. 2012. Twevent: segment-based event detection from tweets. In CIKM, Chen, X. W., Lebanon, G., Wang, H. & Zaki, M. J. (eds). ACM, 155–164. ISBN 978-1-4503-1156-4.CrossRefGoogle Scholar
Li, J., Khan, S. U., Li, Q., Ghani, N., Min-Allah, N., Bouvry, P. & Zhang, W. 2011a. Efficient data sharing over large-scale distributed communities. In Intelligent Decision Systems in Large-Scale Distributed Environments. Springer, 149–164.CrossRefGoogle Scholar
Li, J., Li, Q., Khan, S. U. & Ghani, N. 2011b. Community-based cloud for emergency management. In 2011 6th International Conference on System of Systems Engineering, 55–60.CrossRefGoogle Scholar
Li, P., He, L.,Wang, H., Hu, X., Zhang, Y., Li, L. &Wu, X. 2018. Learning from short text streams with topic drifts. IEEE Transactions on Cybernetics 48(9), 2697–2711. ISSN 2168-2267. doi: 10.1109/TCYB.2017.2748598.CrossRefGoogle ScholarPubMed
Li, S., Wang, Z., Zhou, G. & Lee, S. Y. M. 2011c. Semi-supervised learning for imbalanced sentiment classification. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume 3, IJCAI ’11, 1826–1831. AAAI Press. ISBN 978-1-57735-515-1.Google Scholar
Li, X., Yan, L., Qin, N. & Ran, H. 2017. A novel semi-supervised short text classification algorithm based on fusion similarity. In Intelligent Computing Methodologies, Huang, D.-S., Hussain, A., Han, K. & Gromiha, M. M. (eds). Springer International Publishing, 309–319. ISBN 978-3-319-63315-2.CrossRefGoogle Scholar
Liang, S., Yilmaz, E. & Kanoulas, E. 2016. Dynamic clustering of streaming short documents. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, 995–1004. ACM, New York, NY, USA. ISBN 978-1-4503-4232-2.CrossRefGoogle Scholar
Lifna, C. S. & Vijayalakshmi, M. 2015. Identifying concept-drift in Twitter streams. Procedia Computer Science 45, 86–94. ISSN 1877-0509. International Conference on Advanced Computing Technologies and Applications (ICACTA).Google Scholar
Lin, J., Keogh, E., Lonardi, S. & Chiu, B. 2003. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD ’03, 2–11. ACM.CrossRefGoogle Scholar
Liu, H. & Yu, L. 2005. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502.Google Scholar
Losing, V., Hammer, B. & Wersing, H. 2018. Incremental on-line learning: a review and comparison of state of the art algorithms. Neurocomputing 275,1261–1274. ISSN 0925-2312.Google Scholar
Mathew, K. & Issac, B. 2011. Intelligent spam classification for mobile text message. In 2011 International Conference on Computer Science and Network Technology (ICCSNT), 1, 101–105.CrossRefGoogle Scholar
Miller, Z., Dickinson, B., Deitrick, W., Hu, W. & Wang, A. H. 2013. Twitter spammer detection using data stream clustering. Information Sciences 260, 64–73. ISSN 0020-0255.Google Scholar
Nakov, P., Rosenthal, S., Kiritchenko, S., Mohammad, S. M., Kozareva, Z. Ritter, A., Stoyanov, V. & Zhu, X. 2016. Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts. Language Resources and Evaluation 50(1), 35–65. ISSN 1574-020X.CrossRefGoogle Scholar
Ni, X., Quan, X., Lu, Z., Wenyin, L. & Hua, B. 2011. Short text clustering by finding core terms. Knowledge and Information Systems 27(3), 345–365.CrossRefGoogle Scholar
Nishida, K., Banno, R., Fujimura, K. & Hoshide, T. 2011. Tweet classification by data compression. In Proceedings of the 2011 International Workshop on DETecting and Exploiting Cultural DiversiTy on the Social Web, DETECT ’11, 29–34. ACM. ISBN 978-1-4503-0962-2.CrossRefGoogle Scholar
Oh, O., Agrawal, M. & Rao, H. R. 2011. Information control and terrorism: tracking the Mumbai terrorist attack through Twitter. Information Systems Frontiers 13(1), 33–43. ISSN 1572-9419.CrossRefGoogle Scholar
Parikh, R. & Karlapalem, K. 2013. ET: events from tweets. In WWW (Companion Volume), Carr, L., Laender, A. H. F., Lóscio, B. F. King, I., Fontoura, M., Vrandecic, D., Aroyo, L., de Oliveira, J. P. M., Lima, F. & Wilde, E. (eds), 613–620. International World Wide Web Conferences Steering Committee/ACM. ISBN 978-1-4503-2038-2.Google Scholar
Phan, X.-H., Nguyen, L.-M. & Horiguchi, S. 2008. Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In WWW ’08: Proceeding of the 17th International Conference on World Wide Web, 91–100. ACM. ISBN 978-1-60558-085-2.CrossRefGoogle Scholar
Popovici, R., Weiler, A. & Grossniklaus, M. 2014. On-line clustering for real-time topic detection in social media streaming data. In SNOW-DC@ WWW, 57–63.Google Scholar
Prusa, J., Khoshgoftaar, T. M. & Dittman, D. J. 2015. Using ensemble learners to improve classifier performance on tweet sentiment data. In 2015 IEEE International Conference on Information Reuse and Integration, 252–257.CrossRefGoogle Scholar
Prusa, J. D., Khoshgoftaar, T. M. & Seliya, N. 2016. Enhancing ensemble learners with data sampling on highdimensional imbalanced tweet sentiment data. In FLAIRS Conference, 322–328.Google Scholar
Rangrej, A., Kulkarni, S. & Tendulkar, A. V. 2011. Comparative study of clustering techniques for short text documents. In Proceedings of the 20th International Conference Companion on WorldWideWeb,WWW’11, 111–112. ACM. ISBN 978-1-4503-0637-9.Google Scholar
Ravi, S. & Kozareva, Z. 2018. Self-governing neural networks for on-device short text classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 804–810. Association for Computational Linguistics.Google Scholar
Romero, F. P., Julián-Iranzo, P., Soto, A., Ferreira-Satler, M. & Gallardo-Casero, J. 2013. Classifying unlabeled short texts using a fuzzy declarative approach. Language Resources and Evaluation 47(1), 151–178. ISSN 1574-020X.CrossRefGoogle Scholar
Rosa, K. D. & Ellen, J. 2009. Text classification methodologies applied to micro-text in military chat. In International Conference on Machine Learning and Applications, 2009, ICMLA ’09, 710–714.CrossRefGoogle Scholar
Rosa, K. D., Shah, R., Lin, B., Gershman, A. & Frederking, R. 2011. Topical clustering of tweets. In Proceedings of the ACM SIGIR: SWSM.Google Scholar
Saeys, Y., Inza, I. & Larrañaga, P. 2007. A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517.CrossRefGoogle ScholarPubMed
Sajnani, H., Javanmardi, S., McDonald, D. W. & Lopes, C. V. 2011. Multi-label classification of short text: a study on Wikipedia barnstars. In Analyzing Microtext, AAAI Workshops WS-11-05. AAAI.Google Scholar
Sander, J., Ester, M., Kriegel, H.-P. & Xu, X. 1998. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery 2(2), 169–194.CrossRefGoogle Scholar
Sculley, D. 2010. Web-scale k-means clustering. In Proceedings of the 19th International Conference on WorldWide Web, WWW ’10, 1177–1178. ACM, New York, NY, USA. ISBN 978-1-60558-799-8.CrossRefGoogle Scholar
Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47. ISSN 0360-0300.Google Scholar
Sedhai, S. & Sun, A. 2015. HSpam14: a collection of 14 million tweets for hashtag-oriented spam research. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, 223–232. ACM. ISBN 978-1-4503-3621-5.Google Scholar
Sedhai, S. & Sun, A. 2018. Semi-supervised spam detection in Twitter stream. IEEE Transactions on Computational Social Systems 5(1), 169–175.CrossRefGoogle Scholar
Shi, C., Li, Y., Zhang, J., Sun, Y. & Yu, P. S. 2017. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering 29(1), 17–37. ISSN 1041-4347.CrossRefGoogle Scholar
Song, G., Ye, Y., Du, X., Huang, X. & Bie, S. 2014. Short text classification: a survey. Journal of Multimedia 9(5), 635.CrossRefGoogle Scholar
Stilo, G. & Velardi, P. 2017. Hashtag sense clustering based on temporal similarity. Computational Linguistics, 43(1), 181–200. ISSN 0891-2017.CrossRefGoogle Scholar
Su-zhi, Z. & Pei-feng, S. 2011. A new short-text categorization algorithm based on improved KSVM. In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), 154–157.CrossRefGoogle Scholar
Tang, J. & Liu, H. 2012. Feature selection with linked data in social media. In Proceedings of the 12th SIAM International Conference on Data Mining, 118–128. SIAM/Omnipress. ISBN 978-1-61197-232-0.Google Scholar
Tang, J., Alelyani, S. & Liu, H. 2014. Feature selection for classification: a review. In Data Classification: Algorithms and Applications, Aggarwal, C. C. (ed). CRC Press, 37–64. ISBN 978-1-4665-8674-1.Google Scholar
Thelwall, M., Buckley, K. & Paltoglou, G. 2011. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology 62(2), 406–418. ISSN 1532-2890.CrossRefGoogle Scholar
Tsur, O., Littman, A. & Rappoport, A. 2013. Efficient clustering of short messages into general domains. Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013. 621–630.Google Scholar
Tsymbal, A. 2004. The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin 106(2).Google Scholar
Tu, H. & Ding, J. 2012. An efficient clustering algorithm for microblogging hot topic detection. In 2012 International Conference on Computer Science Service System (CSSS), 738–741.CrossRefGoogle Scholar
Wang, J., Zhao, P., Hoi, S. C. H. & Jin, R. 2014. Online feature selection and its applications. IEEE Transactions on Knowledge and Data Engineering 26(3), 698–710. ISSN 1041-4347.Google Scholar
Wang, P., Xu, B., Xu, J., Tian, G., Liu, C.-L. & Hao, H. 2016. Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification. Neurocomputing 174, 806–814a. ISSN 0925-2312.Google Scholar
Wang, Z., Mi, H. & Ittycheriah, A. 2016b. Semi-supervised clustering for short text via deep representation learning. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, August 11–12, 2016, 31–39.CrossRefGoogle Scholar
Weller, K., Bruns, A., Burgess, J. & Mahrt, M. 2013. Twitter and Society. Peter Lang International Academic Publishers. ISBN 1433121697, 9781433121692.Google Scholar
Weng, J. & Lee, B.-S. 2011. Event detection in Twitter. In Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, July 17–21, 2011, Adamic, L. A., Baeza-Yates, R. A. &Counts, S. (eds). The AAAI Press.Google Scholar
Witten, I. H., Frank, E., Hall, M. A. & Pal, C. J. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.Google Scholar
Wu, W., Li, H., Wang, H. & Zhu, K. Q. 2012. Probase: a probabilistic taxonomy for text understanding. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, 481–492. ACM. ISBN 978-1-4503-1247-9.CrossRefGoogle Scholar
Xu, R. & Wunsch, D. 2005. Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678. ISSN 1045-9227.Google Scholar
Yan, L., Zheng, Y.&Cao., J. 2018. Few-shot learning for short text classification. Multimedia Tools and Applications 77(22), 29799–29810. ISSN 1573-7721.CrossRefGoogle Scholar
Yang, C. C. & Ng, T. D. 2009. Web opinions analysis with scalable distance-based clustering. In ISI, 65–70. IEEE.Google Scholar
Yang, L., Li, C., Ding, Q. & Li, L. 2013. Combining lexical and semantic features for short text classification. Procedia Computer Science 22:78–86. ISSN 1877-0509. 17th International Conference on Knowledge Based and Intelligent Information and Engineering Systems - KES 2013.CrossRefGoogle Scholar
Yin, J. & Wang, J. 2014. A Dirichlet multinomial mixture model-based approach for short text clustering. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, 233–242. ACM. ISBN 978-1-4503-2956-9.Google Scholar
Yin, J., Chao, D., Liu, Z., Zhang, W., Yu, X. & Wang, J. 2018. Model-based clustering of short text streams. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2634–2642. ACM.CrossRefGoogle Scholar
Yu, Y. & Chen, Y. 2012. A novel content based and social network aided online spam short message filter. In 2012 10th World Congress on Intelligent Control and Automation (WCICA), 444–449.Google Scholar
Yuan, Q., Cong, G. & Magnenat-Thalmann, N. 2012. Enhancing naive bayes with various smoothing methods for short text classification. In WWW (Companion Volume), Mille, A., Gandon, Misselis, F. L. J., Rabinovich, M. & Staab, S. (eds). ACM, 645–646. ISBN 978-1-4503-1230-1.Google Scholar
Zhai, C. & Lafferty, J. 2004. A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems 22(2), 179–214. ISSN 1046-8188.CrossRefGoogle Scholar
Zhang, G., Sun, Y., Xu, M. & Bie, R. 2014. Weibo clustering: a new approach utilizing users’ reposting data in social networking services. Computer Science and Information Systems 11(3), 1157–1172.CrossRefGoogle Scholar
Zhang, H. & Zhong, G. 2016. Improving short text classification by learning vector representations of both words and hidden topics. Knowledge-Based Systems 102, 76–86. ISSN 0950-7051.CrossRefGoogle Scholar
Zubiaga, A., Liakata, M., Procter, R., Bontcheva, K. & Tolmie, P. 2015. Towards detecting rumours in social media. In AAAI Workshop: AI for Cities.Google Scholar