Abstract
With the proliferation of fake news and misinformation on social media, being able to differentiate a reliable source of information has become increasingly important. In this paper we present a new algorithm for filtering expert users in social networks according to a certain topic under study. For the algorithm fine-tuning, a comparative study of results according to different word embeddings as well as different representation models, such as Skip-Gram and CBOW, is provided alongside the paper.
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Abu-Salih, B., Wongthongtham, P., Chan, K.Y., Zhu, D.: CredSaT: credibility ranking of users in big social data incorporating semantic analysis and temporal factor. J. Inf. Sci. 45(2), 259–280 (2019)
Alrubaian, M., Al-Qurishi, M., Hassan, M.M., Alamri, A.: A credibility analysis system for assessing information on twitter. IEEE Trans. Dependable Secure Comput. 15(4), 661–674 (2018). https://doi.org/10.1109/TDSC.2016.2602338
Alrubaian, M., AL-Qurishi, M., Alrakhami, M., Hassan, M., Alamri, A.: Reputation-based credibility analysis of twitter social network users: reputation-based credibility analysis of twitter social network users. Concurr. Comput. Pract. Exp. 29, e3873 (2016). https://doi.org/10.1002/cpe.3873
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
Chretien, K.C., Tuck, M.G., Simon, M., Singh, L.O., Kind, T.: A digital ethnography of medical students who use twitter for professional development. J. Gen. Intern. Med. 30(11), 1673–1680 (2015)
Diaz, F., Mitra, B., Craswell, N.: Query expansion with locally-trained word embeddings. arXiv preprint arXiv:1605.07891 (2016)
Diaz-Garcia, J.A., Fernandez-Basso, C., Ruiz, M.D., Martin-Bautista, M.J.: Mining text patterns over fake and real tweets. In: Lesot, M.-J., et al. (eds.) IPMU 2020. CCIS, vol. 1238, pp. 648–660. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50143-3_51
Forte, A., Humphreys, M., Park, T.: Grassroots professional development: how teachers use twitter. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 6 (2012)
Gerstein, J.: The use of twitter for professional growth and development. Int. J. E-Learn. 10(3), 273–276 (2011)
Ghosh, S., Sharma, N., Benevenuto, F., Ganguly, N., Gummadi, K.: Cognos: crowdsourcing search for topic experts in microblogs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 575–590 (2012)
Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223), 497–506 (2020)
Kuzi, S., Shtok, A., Kurland, O.: Query expansion using word embeddings. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1929–1932 (2016)
Lamsal, R.: Coronavirus (COVID-19) tweets dataset (2020). https://doi.org/10.21227/781w-ef42
Levy, O., Goldberg, Y.: Dependency-based word embeddings. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 302–308 (2014)
Liu, Y., Liu, Z., Chua, T.S., Sun, M.: Topical word embeddings. In: Twenty-Ninth AAAI Conference on Artificial Intelligence. Citeseer (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural. Inf. Process. Syst. 26, 3111–3119 (2013)
Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162
Roy, D., Paul, D., Mitra, M., Garain, U.: Using word embeddings for automatic query expansion. arXiv preprint arXiv:1606.07608 (2016)
Vrehuuvrek, R., Sojka, P., et al.: Gensim-statistical semantics in Python. Retrieved from Genism (2011)
Zhou, P., et al.: A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579(7798), 270–273 (2020)
Acknowledgments
The research reported in this paper was partially supported by the COPKIT project under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 786687), the Andalusian government and the FEDER operative program under the project BigDataMed (P18-RT-2947 and B-TIC-145-UGR18). Finally the project is also partially supported by the Spanish Ministry of Education, Culture and Sport (FPU18/00150).
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Diaz-Garcia, J.A., Ruiz, M.D., Martin-Bautista, M.J. (2021). A Comparative Study of Word Embeddings for the Construction of a Social Media Expert Filter. In: Andreasen, T., De Tré, G., Kacprzyk, J., Legind Larsen, H., Bordogna, G., Zadrożny, S. (eds) Flexible Query Answering Systems. FQAS 2021. Lecture Notes in Computer Science(), vol 12871. Springer, Cham. https://doi.org/10.1007/978-3-030-86967-0_15
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