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ALONE: A Dataset for Toxic Behavior Among Adolescents on Twitter

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Social Informatics (SocInfo 2020)

Abstract

The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive explanation. Each instance of interaction includes tweets, images, emoji and related metadata. Our observations show that individual tweets do not provide sufficient evidence for toxic behavior, and meaningful use of context in interactions can enable highlighting or exonerating tweets with purported toxicity.

T. Wijesiriwardene and H. Inan—Equally contributed.

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Notes

  1. 1.

    https://www.pewresearch.org/fact-tank/2017/07/11/key-takeaways-online-harassment/.

  2. 2.

    https://www.cim.co.uk/newsroom/release-half-of-teens-exposed-to-harmful-social-media/.

  3. 3.

    https://www.cnn.com/2016/12/14/health/teen-suicide-cyberbullying-continues-trnd/index.html.

  4. 4.

    https://www.theguardian.com/technology/blog/2014/jul/23/subtweeting-what-is-it-and-how-to-do-it-well.

  5. 5.

    http://bolobhi.org/abuse-subtweeting-tweet-school-cyber-bullying/.

  6. 6.

    https://github.com/onnx/models/tree/master/vision/classification/resnet.

  7. 7.

    http://wiki.aiisc.ai/index.php/EmojiNet.

  8. 8.

    http://homepages.inf.ed.ac.uk/jeanc/maptask-coding-html/node23.html.

References

  1. Arpinar, I.B., Kursuncu, U., Achilov, D.: Social media analytics to identify and counter islamist extremism: systematic detection, evaluation, and challenging of extremist narratives online. In: 2016 International Conference on Collaboration Technologies and Systems (CTS), pp. 611–612. IEEE (2016)

    Google Scholar 

  2. Arseneault, L., Bowes, L., Shakoor, S.: Bullying victimization in youths and mental health problems: “much ado about nothing”? Psychol. Med. 40, 717 (2010)

    Article  Google Scholar 

  3. Badjatiya, P., Gupta, M., Varma, V.: Stereotypical bias removal for hate speech detection task using knowledge-based generalizations. In: The World Wide Web Conference, pp. 49–59 (2019)

    Google Scholar 

  4. Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: WWW (2017)

    Google Scholar 

  5. Brener, N.D., Simon, T.R., Krug, E.G., Lowry, R.: Recent trends inviolence-related behaviors among high school students in the United States. JAMA 282, 440–446 (1999)

    Article  Google Scholar 

  6. Buss, A.H.: The psychology of aggression (1961)

    Google Scholar 

  7. Carletta, J., Isard, A., Isard, S., Kowtko, J.C., Doherty-Sneddon, G., Anderson, A.H.: The reliability of a dialogue structure coding scheme (1997)

    Google Scholar 

  8. Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., Vakali, A.: Mean birds: detecting aggression and bullying on twitter. In: ACM Web Science (2017)

    Google Scholar 

  9. Crumback, D.: Subtweets: the new online harassment (2017)

    Google Scholar 

  10. Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: AAAI-ICWSM (2017)

    Google Scholar 

  11. Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: AAAI-ICWSM (2011)

    Google Scholar 

  12. Duong, C.T., Lebret, R., Aberer, K.: Multimodal classification for analysing social media. arXiv preprint arXiv:1708.02099 (2017)

  13. Edwards, A., Harris, C.J.: To tweet or “subtweet”?: impacts of social networking post directness and valence on interpersonal impressions. Comput. Hum. Behav. 63, 304–310 (2016)

    Article  Google Scholar 

  14. Founta, A., et al.: Large scale crowdsourcing and characterization of Twitter abusive behavior (2018)

    Google Scholar 

  15. Gaur, M., et al.: Knowledge-aware assessment of severity of suicide risk for early intervention. In: The World Wide Web Conference, pp. 514–525. ACM (2019)

    Google Scholar 

  16. Golbeck, J., et al.: A large labeled corpus for online harassment research. In: ACM Web Science (2017)

    Google Scholar 

  17. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  18. Hosseinmardi, H., Mattson, S.A., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Analyzing labeled cyberbullying incidents on the Instagram social network. In: SocInfo (2015)

    Google Scholar 

  19. Jay, T., Janschewitz, K.: The pragmatics of swearing. J. Polit. Res. Lang. Behav. Cult. 4, 267–288 (2008)

    Google Scholar 

  20. Kumpulainen, K., Räsänen, E., Puura, K.: Psychiatric disorders and the use of mental health services among children involved in bullying. Aggressive Behav. J. 27, 102–110 (2001)

    Article  Google Scholar 

  21. Kursuncu, U.: Modeling the persona in persuasive discourse on social media using context-aware and knowledge-driven learning. Ph.D. thesis, University of Georgia (2018)

    Google Scholar 

  22. Kursuncu, U., et al.: Modeling islamist extremist communications on social media using contextual dimensions: religion, ideology, and hate. In: Proceedings of the ACM on Human-Computer Interaction, vol. 3,no. CSCW, pp. 1–22 (2019)

    Google Scholar 

  23. Kursuncu, U., et al.: What’s ur type? Contextualized classification of user types in marijuana-related communications using compositional multiview embedding. In: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474–479. IEEE (2018)

    Google Scholar 

  24. Kursuncu, U., Gaur, M., Lokala, U., Thirunarayan, K., Sheth, A., Arpinar, I.B.: Predictive analysis on Twitter: techniques and applications. In: Agarwal, N., Dokoohaki, N., Tokdemir, S. (eds.) Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. LNSN, pp. 67–104. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94105-9_4

    Chapter  Google Scholar 

  25. Kursuncu, U., Gaur, M., Sheth, A.: Knowledge infused learning (K-IL): towards deep incorporation of knowledge in deep learning. In: Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice. Stanford University, Palo Alto, California, USA. AAAI-MAKE (2020)

    Google Scholar 

  26. Liu, J., Lewis, G., Evans, L.: Understanding aggressive behaviour across the lifespan. J. Psychiatric Ment. Health Nurs. 20, 156–168 (2013)

    Article  Google Scholar 

  27. Lowry, R., Powell, K.E., Kann, L., Collins, J.L., Kolbe, L.J.: Weapon-carrying, physical fighting, and fight-related injury among us adolescents. Am. J. Prevent. Med. 14, 122–129 (1998)

    Article  Google Scholar 

  28. Mishna, F., Schwan, K.J., Lefebvre, R., Bhole, P., Johnston, D.: Students in distress: unanticipated findings in a cyber bullying study. Child. Youth Serv. Rev. 44, 341–348 (2014)

    Article  Google Scholar 

  29. Namie, G., Namie, R.: Bully at work: what you can do to stop the hurt and reclaim your dignity on the job (2009)

    Google Scholar 

  30. Nilan, P., Burgess, H., Hobbs, M., Threadgold, S., Alexander, W.: Youth, social media, and cyberbullying among australian youth: “sick friend”. Soc. Media + Soc. 1, 2056305115604848 (2015)

    Article  Google Scholar 

  31. Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., Chang, Y.: Abusive language detection in online user content. In: WWW (2016)

    Google Scholar 

  32. O’Halloran, K., Chua, A., Podlasov, A.: The role of images in social media analytics: a multimodal digital humanities approach. In: Visual communication (2014)

    Google Scholar 

  33. Papegnies, E., Labatut, V., Dufour, R., Linarès, G.: Detection of abusive messages in an on-line community. In: CORIA (2017)

    Google Scholar 

  34. Parent, M.C., Gobble, T.D., Rochlen, A.: Social media behavior, toxic masculinity, and depression. Psychol. Men Masculinities 20(3), 277 (2019)

    Article  Google Scholar 

  35. Patchin, J.W., Hinduja, S.: Bullies move beyond the schoolyard: a preliminary look at cyberbullying. Youth Violence Juvenile Justice 4, 148–169 (2006)

    Article  Google Scholar 

  36. Rafla, M., Carson, N.J., DeJong, S.M.: Adolescents and the internet: what mental health clinicians need to know. Curr. Psychiatry Rep. 16(9), 472 (2014)

    Article  Google Scholar 

  37. Razavi, A.H., Inkpen, D., Uritsky, S., Matwin, S.: Offensive language detection using multi-level classification. In: Farzindar, A., Kešelj, V. (eds.) AI 2010. LNCS (LNAI), vol. 6085, pp. 16–27. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13059-5_5

    Chapter  Google Scholar 

  38. Rezvan, M., Shekarpour, S., Alshargi, F., Thirunarayan, K., Shalin, V.L., Sheth, A.: Analyzing and learning the language for different types of harassment. PLoS One 15(3), e0227330 (2020)

    Article  Google Scholar 

  39. Rezvan, M., Shekarpour, S., Balasuriya, L., Thirunarayan, K., Shalin, V.L., Sheth, A.: A quality type-aware annotated corpus and lexicon for harassment research. In: ACM Web Science (2018)

    Google Scholar 

  40. Rivers, I., Poteat, V.P., Noret, N., Ashurst, N.: Observing bullying at school: the mental health implications of witness status. School Psychol. Quart. 24, 211 (2009)

    Article  Google Scholar 

  41. Safadi, H., et al.: Curtailing fake news propagation with psychographics. Available atSSRN 3558236 (2020)

    Google Scholar 

  42. Salminen, J., et al.: Anatomy of online hate: developing a taxonomy and machine learning models for identifying and classifying hate in online news media. In: ICWSM, pp. 330–339 (2018)

    Google Scholar 

  43. Sharma, S., Agrawal, S., Shrivastava, M.: Degree based classification of harmful speech using Twitter data. arXiv preprint arXiv:1806.04197 (2018)

  44. Silva, L., Mondal, M., Correa, D., Benevenuto, F., Weber, I.: Analyzing the targets of hate in online social media. In: AAAI-ICWSM (2016)

    Google Scholar 

  45. Soberón, G., Aroyo, L., Welty, C., Inel, O., Lin, H., Overmeen, M.: Measuring crowd truth: disagreement metrics combined with worker behavior filters. In: CrowdSem 2013 Workshop (2013)

    Google Scholar 

  46. Søndergaard, D.M.: Bullying and social exclusion anxiety in schools. Br. J. Sociol. Educ. 33, 55–372 (2012)

    Article  Google Scholar 

  47. Unicef, et al.: An everyday lesson: end violence in schools (2018)

    Google Scholar 

  48. Viner, R.M., et al.: Roles of cyberbullying, sleep, and physical activity in mediating the effects of social media use on mental health and wellbeing among young people in England: a secondary analysis of longitudinal data. Lancet Child Adolescent Health 3, 685–696 (2019)

    Article  Google Scholar 

  49. Wandersman, A., Nation, M.: Urban neighborhoods and mental health: psychological contributions to understanding toxicity, resilience, and interventions. Am. Psychol. 53(6), 647 (1998)

    Article  Google Scholar 

  50. Warner, W., Hirschberg, J.: Detecting hate speech on the world wide web. In: ACL (2012)

    Google Scholar 

  51. Waseem, Z.: Are you a racist or am i seeing things? Annotator influence on hate speech detection on twitter. In: NLP-CSS (2016)

    Google Scholar 

  52. Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In: NAACL (2016)

    Google Scholar 

  53. Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D.: EmojiNet: an open service and API for emoji sense discovery. In: AAAI-ICWSM (2017)

    Google Scholar 

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Acknowledgement

We acknowledge partial support from the National Science Foundation (NSF) award CNS-1513721: “Context-Aware Harassment Detection on Social Media”. Any opinions, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

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Correspondence to Ugur Kursuncu .

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Wijesiriwardene, T. et al. (2020). ALONE: A Dataset for Toxic Behavior Among Adolescents on Twitter. In: Aref, S., et al. Social Informatics. SocInfo 2020. Lecture Notes in Computer Science(), vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-60975-7_31

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