On the Characterization of Digital Trolls from Twitter Big Data

Authors

  • Ahmed khudhair Abbas University of Diyala Author
  • Hayder Hassan Safi Mustansiriyah University Author
  • Ali Hasan Taresh University of Information Technology and Communications (UoITC) Author

DOI:

https://doi.org/10.61841/a5kenj54

Keywords:

Trolls, Twitter, Big data, social network

Abstract

Recently, Twitter has become one of the most common and effective social media tools in our lives. People use Twitter to share their opinions, feelings, and orientations, especially during political unrest or protests. In order to disrupt the protest operations on Twitter or to influence public opinion, electronic flies (trolls) are widely and effectively used. Accordingly, there is a need to find a method that can automatically and precisely detect these accounts and isolate them from Twitter. Moreover, detecting and characterizing these accounts becomes a significant task to reduce or mitigate its effect on the real general opinion. This paper presents an intensive analysis that can be utilized to effectively detect the troll accounts and isolate their bad effects from Twitter. We considered the public trolls account datasets published by Twitter, and we also gathered a new dataset from Twitter that includes tweets and users’ information from different countries to make a fair analysis for the trolls’ accounts. The results show that the suspicious activities of Twitter troll accounts can be used to detect most of these accounts automatically without using sentiment analysis and opinion mining techniques with an accuracy of 95%. To accomplish this task, we propose a set of robust and efficient features that can accurately characterize troll accounts with a relatively small number of features. 

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References

[1] Buhl, Hans Ulrich, et al., "Big data." (2013): 63-68.

[2] Gil Press, May 9, 2013, 09:45am, A Very Short History Of Big Data, available: https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#8a6dc8665a18

[3] Banks, David L., and Nicholas Hengartner. "Social networks." Encyclopedia of Quantitative Risk Analysis and Assessment 4 (2008).

[4] Kolleck, Nina. "Social network analysis in innovation research: using a mixed methods approach to analyze social innovations." European Journal of Futures Research 1, no. 1 (2013): 25. https://doi.org/10.1007/s40309-013-0025-2

[5] Knoke, David, and Song Yang. Social network analysis. Sage Publications, 2019.

[6] Twitter, Inc. "Twitter." URL: https://twitter.com/SenBlumenthal/status/1175102777351122945 (Data obrashcheniya: 20.10.2019) (2010).

[7] Lewis et al., 2011 P. Lewis, R. Rezaie, R. Brown, N. Roberts, R.I.M. Dunbar Ventromedial prefrontal volume predicts understanding of others and social network size Neuroimaging, 57 (4) (2011), pp. 1624-1629

[8] Alsmadi, Izzat, and Michael J. O'Brien. "How Many Bots in Russian Troll Tweets?." Information Processing & Management 57.6 (2020): 102303.

[9] Sahmoud, Shaaban, and Hayder Safi. "Detecting Suspicious Activities of Digital Trolls During the Political Crisis." 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). IEEE, 2020.

[10] Fornacciari, Paolo, et al. "A holistic system for troll detection on Twitter." Computers in Human Behavior 89 (2018): 258-268.

[11] Alhazbi, Saleh. "Behavior-Based Machine Learning Approaches to Identify State-Sponsored Trolls on Twitter." IEEE Access 8 (2020): 195132-195141.

[12] Engelin, Martin, and Felix De Silva. "Troll detection: A comparative study in detecting troll farms on Twitter using cluster analysis." (2016).

[13] Badawy, Adam, et al., "Characterizing the 2016 Russian IRA influence campaign." Social Network Analysis and Mining 9.1 (2019): 1-11.

[14] Ghanem, Bilal, Davide Buscaldi, and Paolo Rosso. "TexTrolls: Identifying Russian trolls on Twitter from a textual perspective." arXiv preprint arXiv:1910.01340 (2019).

[15] Kim, Dongwoo, et al., "Analysing user identity via time-sensitive semantic edit distance (t-SED): a case study of Russian trolls on Twitter." Journal of Computational Social Science 2.2 (2019): 331-351.

[16] Abbott, Andrew, and Angela Tsay. "Sequence analysis and optimal matching methods in sociology: review and prospect." Sociological methods & research 29.1 (2000): 3-33.

[17] Kellner, Ansgar, Christian Wressnegger, and Konrad Rieck. "What's all that noise: analysis and detection of propaganda on Twitter." Proceedings of the 13th European workshop on Systems Security, 2020.

[18] Broniatowski, David A., et al., "Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate." American journal of public health 108.10 (2018): 1378-1384.

[19] Zannettou, Savvas, et al., "Characterizing the use of images by state-sponsored troll accounts on Twitter." arXiv preprint arXiv:1901.05997 (2019).

[20] Makice, Kevin. Twitter API: Up and running: Learn how to build applications with the Twitter API. "O'Reilly Media, Inc.", 2009.

[21] Berthold, Michael R., et al., "KNIME—the Konstanz information miner: version 2.0 and beyond." AcM SIGKDD Explorations Newsletter 11.1 (2009): 26-31.

[22] Website: https://github.com/twintproject/twint, Date: 10/12/2019

[23] Kozma, Laszlo. "k Nearest Neighbors algorithm (kNN)." Helsinki University of Technology (2008).

[24] Kleinbaum, David G., et al., Logistic regression. New York: Springer-Verlag, 2002.

[25] Noble, William S. "What is a support vector machine?." Nature Biotechnology 24.12 (2006): 1565-1567.

[26] Steinberg, Dan. "CART: classification and regression trees." The top ten algorithms in data mining. Chapman and Hall/CRC, 2009. 193-216.

[27] Kumar, Akshi, and Teeja Mary Sebastian. "Sentiment analysis on Twitter." International Journal of Computer Science Issues (IJCSI) 9.4 (2012): 372.

[28] Jamali, Mohsen, and Hassan Abolhassani. "Different aspects of social network analysis." 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings) (WI'06). IEEE, 2006.

[29] Paavola, Jarkko, et al., "Understanding the trolling phenomenon: The automated detection of bots and cyborgs in the social media." Journal of Information Warfare 15.4 (2016): 100-111.

[30] Zheng, Alice, and Amanda Casari. Feature engineering for machine learning: principles and techniques for data scientists. "O'Reilly Media, Inc.", 2018.

[31] Dong, Guozhu, and Huan Liu, eds. Feature engineering for machine learning and data analytics. CRC Press, 2018.

[32] Twitter Transparency Project, https://transparency.twitter.com/, retrieved in 20-12-2019

[33] Albahli, Approach Saleh, et al., "COVID-19 public sentiment insights: A text mining approach to the Gulf countries." Cmc-Computers Materials & Continua (2020): 1613-1627.

[34] Albahli, Saleh, et al., "Predicting the type of crime: intelligence gathering and crime analysis." Computers, Materials & Continua 66.3 (2020): 2317-2341.

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Published

30.04.2020

How to Cite

khudhair Abbas, A., Hassan Safi, H., & Hasan Taresh, A. (2020). On the Characterization of Digital Trolls from Twitter Big Data. International Journal of Psychosocial Rehabilitation, 24(2), 9743-9756. https://doi.org/10.61841/a5kenj54