Abstract
In this paper, we investigate the problem of the rumor detection for Russian language. For experiments, we collected messages in Twitter in Russian. We implemented a set of features and trained a neural network on the dataset about three thousand tweets, collected and annotated by us. 40% of this collection contains rumors of three events. The software for rumor detection in tweets was developed. We used SVM to filter tweets by type of speech act. An experiment was conducted to check the tweet for rumor with a calculation of accuracy, precision and recall values. F1 measure reached the value 0.91.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alton, Y.K., Snehasish, B.: Linguistic predictors of rumor veracity on the internet. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2016 IMECS, vol. 1, pp. 387–391 (2016)
Crystal, D.: Language and the Internet. Cambridge University Press, Cambridge (2006)
Diab, M., Sardar, H.: Rumor detection and classification for twitter data, pp. 71–77 (2015)
Friggeri, A., Adamic, L.A., Eckle,s D., Cheng J.: Rumor cascades, pp. 101–110 (2015)
Goel, S., Watts, D.J., Goldstein, D.G.: The structure of online diffusion networks. In: Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 623–638 (2012)
Jin, C., Zhang, L.: News verification by exploiting conflicting, pp. 2972–2978 (2016)
Jing, M., et al.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 3818–3824 (2016)
Kirk, M.: Thoughtful Machine Learning with Python, vol. 1, p. 220. O’Reilly Media, Sebastopol (2017)
Kurt, T., Chris, G., Dawn, S., Vern, A.P.: Suspended accounts in retrospect: an analysis of twitter spam. In: Proceedings of the ACM SIGCOMM Conference on Internet Measurement Conference, pp. 243–258 (2011)
Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y. Prominent features of rumor propagation in online social media. In: International Conference, pp. 1103–1108 (2013)
Liu, L., Qu, Q., Chen, B., Hanjalic, A., Wang, H.: Modelling of information diffusion on social networks with applications to WeChat. Phys. A Stat. Mech. Appl. 496, 318–329 (2018)
Mozdeh Big Data Text Analysis. http://mozdeh.wlv.ac.uk. Accessed 01 June 2019
Pandas: powerful Python data analysis toolkit. http://pandas.pydata.org/pandas-docs/stable/index.html. Accessed 01 June 2019
Paramita, M.: Extracting temporal and causal relations between events. In: Proceedings of the ACL 2014 Student Research Workshop, pp. 10–17 (2014)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Qazvinian, V., Rosengren, E., Redev, D., Qiaozhu, M.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599 (2011)
Searle, J.R.: Expression and Meaning: Studies in the Theory of Speech Acts. Cambridge University Press, Cambridge (1985)
Soroush, V.: Automatic Detection and Verification of Rumors. Massachusetts Institute of Technology, Massachusetts (2015)
Takahashi, T., Igata, N.: Rumor detection on twitter. Soft Comput. Intell. Syst. (SCIS) 6, 452–457 (2012)
Twitter libraries — Twitter Developers. https://developer.twitter.com/en/docs/developer-utilities/twitter-libraries.html. Accessed 01 June 2019
Weiling, C., Zhanga, Y., Tong, C., Bu, S.: Unsupervised rumor detection based on users’ behaviors using neural networks. Pattern Recogn. Lett. 105, 226–233 (2018)
Wikipedia contributors. List of emoticons, Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Listofemoticons. Accessed 01 June 2019
Wierzbicka, A.: English Speech Act Verbs: A Semantic Dictionary. Academic Press, Sydney (1987)
Zhang, R., Gao, D., Li, W.: What are tweeters doing: recognizing speech acts in twitter. Analyzing Microtext (2011)
Zhao, X., Jiang, J.: An empirical comparison of topics in twitter and traditional media. Singapore Management University School of Information Systems Technical paper series (2011)
Zhao, Z., Resnick, P., Mei, Q.: Enquiring minds: early detection of rumors in social media from enquiry posts. In: IW3C2 (2015)
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R.: Detection and resolution of rumours in social media: a survey. ACM Comput. Surv. 51, 2–38 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chernyaev, A., Spryiskov, A., Ivashko, A., Bidulya, Y. (2020). A Rumor Detection in Russian Tweets. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2020. Lecture Notes in Computer Science(), vol 12335. Springer, Cham. https://doi.org/10.1007/978-3-030-60276-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-60276-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60275-8
Online ISBN: 978-3-030-60276-5
eBook Packages: Computer ScienceComputer Science (R0)