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Social Network Analysis and Mining

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01.12.2023 | Original Paper

An NLP-assisted Bayesian time-series analysis for prevalence of Twitter cyberbullying during the COVID-19 pandemic

verfasst von: Christopher Perez, Sayar Karmakar

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2023

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Abstract

COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data were also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.

Vorheriger Artikel Gender dynamics on Twitter during the 2020 U.S. Democratic presidential primary

Nächster Artikel Diachronic profile of startup companies through social media

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https://www.businessofapps.com/data/twitter-statistics/.

https://developer.twitter.com/en/docs/twitter-api.

https://trends.google.com/.

https://developer.twitter.com/en/docs/twitter-api/tweets/counts/introduction.

https://huggingface.co/cardiffnlp/twitter-roberta-base-offensive.

https://huggingface.co/cardiffnlp/twitter-roberta-base-hate.

https://research.google.com/colaboratory/.

https://huggingface.co/course/chapter0/1?fw=pt.

Aboujaoude E, Savage Matthew W, Starcevic Vladan, Salame Wael O (2015) Cyberbullying: review of an old problem gone viral. J Adolesc Health 57(1):10–18CrossRef

Babvey P, Capela F, Cappa C, Lipizzi C, Petrowski N, Ramirez-Marquez J (2021) Using social media data for assessing children’s exposure to violence during the covid-19 pandemic. Child Abuse Neglect 116:104747CrossRef

Bacher-Hicks A, Goodman J, Green JG, Holt M (2022) The covid-19 pandemic disrupted both school bullying and cyberbullying. Technical report, National Bureau of Economic Research

Barbieri F, Camacho-Collados J, Espinosa-Anke L, Neves L, (2020) TweetEval: Unified benchmark and comparative evaluation for tweet classification. In: Proceedings of Findings of EMNLP

Barlett Christopher P (2017) From theory to practice: cyberbullying theory and its application to intervention. Comput Human Behav 72:269–275CrossRef

Barlett CP, Rinker A, Roth B (2021) Cyberbullying perpetration in the covid-19 era: an application of general strain theory. J Soc Psychol 161(4):466–476CrossRef

Barlett CP, Simmers MM, Roth B, Gentile D (2021) Comparing cyberbullying prevalence and process before and during the covid-19 pandemic. J Soc Psychol 161(4):408–418CrossRef

Basile V, Bosco C, Fersini E, Nozza D, Patti V, Pardo FMR, Rosso P, Sanguinetti M, et al (2019) Semeval-2019 task 5: multilingual detection of hate speech against immigrants and women in twitter. In: 13th International workshop on semantic evaluation, pp 54–63. Association for Computational Linguistics

Belchior Mota Daniela Cristina, Yury Vasconcellos, da Silva Thaís, Costa Aparecida Ferreira, Helena Magna, da Cunha Aguiar, Maria Eduarda de Melo Marques, and Ricardo Manes Monaquezi, (2021) Mental health and internet use by university students: coping strategies in the context of covid-19. Ciência & Saúde Coletiva 26:2159–2170

Bonanno Rina A, Shelley H (2013) Cyber bullying and internalizing difficulties: above and beyond the impact of traditional forms of bullying. J Youth Adolesc 42(5):685–697CrossRef

Bozkurt A, Jung I, Xiao J, Vladimirschi V, Schuwer R, Egorov G, Lambert S, Al-Freih M, Pete J, Olcott Jr D et al (2020) A global outlook to the interruption of education due to covid-19 pandemic: navigating in a time of uncertainty and crisis. Asian J Distance Edu 15(1):1–126

Candela M, Luconi V, Vecchio A (2020) Impact of the covid-19 pandemic on the internet latency: a large-scale study. Comput Netw 182:107495CrossRef

Cheng L, Li J, Silva YN, Hall DL, Liu H (2019) Xbully: Cyberbullying detection within a multi-modal context. In: Proceedings of the twelfth acm international conference on web search and data mining, pp 339–347

Cheng L, Guo R, Silva Y, Hall D, Liu H (2019) Hierarchical attention networks for cyberbullying detection on the instagram social network. In: Proceedings of the 2019 SIAM international conference on data mining, p 235–243. SIAM

Cornell D, Klein J, Konold T, Huang F (2012) Effects of validity screening items on adolescent survey data. Psychol Assess 24(1):21CrossRef

Cortis K, Handschuh S (2015) Analysis of cyberbullying tweets in trending world events. In: Proceedings of the 15th International conference on knowledge technologies and data-driven business, pp 1–8

Dadvar M, Trieschnigg D, Ordelman R, De Jong F (2013) Improving cyberbullying detection with user context. In: Advances in information retrieval: 35th European conference on IR research, ECIR 2013, Moscow, Russia, 24–27 March, 2013. Proceedings 35

Das S, Kim A, Karmakar S, (2020) Change-point analysis of cyberbullying-related twitter discussions during covid-19. arXiv preprint arXiv:2008.13613

Davidson T, Bhattacharya D, Weber I (2019) Racial bias in hate speech and abusive language detection datasets. arXiv preprint arXiv:1905.12516

Gayo-Avello D, Metaxas P, Mustafaraj E (2011) Limits of electoral predictions using twitter. In: Proceedings of the International AAAI conference on web and social media vol. 5, pp 490–493

Huang Q,Singh VK, Atrey PK (2014) Cyber bullying detection using social and textual analysis. In: Proceedings of the 3rd International workshop on socially-aware multimedia, pp 3–6

Jain O, Gupta M, Satam S, Panda S (2020) Has the covid-19 pandemic affected the susceptibility to cyberbullying in india? Comput Human Behav Rep 2:100029CrossRef

Karmakar S, Das S (2020) Evaluating the impact of covid-19 on cyberbullying through bayesian trend analysis. In: Proceedings of the European interdisciplinary cybersecurity conference, pp 1–6

Karmakar S, Das S, (2021) Understanding the rise of twitter-based cyberbullying due to covid-19 through comprehensive statistical evaluation. In: Proceedings of the 54th Hawaii international conference on system sciences

Kowalski Robin M, Giumetti Gary W, Schroeder Amber N, Lattanner Micah R (2014) Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull 140(4):1073CrossRef

Kwan I, Dickson K, Richardson M, MacDowall W, Burchett H, Stansfield C, Brunton G, Sutcliffe K, Thomas J, (2020) Cyberbullying and children and young people’s mental health: a systematic map of systematic reviews. Cyberpsychol Behav Soc Netw 23(2):72–82CrossRef

Li Y, Goodell JW, Shen D (2021) Comparing search-engine and social-media attentions in finance research: evidence from cryptocurrencies. Int Rev Econ Financ 75:723–746CrossRef

McClymont H, Wenbiao H (2021) Weather variability and covid-19 transmission: a review of recent research. Int J Environ Res Public Health 18(2):396CrossRef

McHugh Meaghan C, Saperstein Sandra L, Gold Robert S (2019) Omg u# cyberbully! an exploration of public discourse about cyberbullying on twitter. Health Edu Behav 46(1):97–105CrossRef

Mike T (2015) Evaluating the comprehensiveness of twitter search api results: a four step method. Cybermetr Int J Scientometr Informetr Bibliometr 18–19:1

Morstatter F, Pfeffer J, Liu H, Carley K (2013) Is the sample good enough? Comparing data from twitter’s streaming api with twitter’s firehose. In: Proceedings of the international AAAI conference on web and social media vol., 7, pp 400–408

Nand P, Perera R, Kasture A (2016) “how bullying is this message?”: a psychometric thermometer for bullying. In: Proceedings of COLING 2016, the 26th International conference on computational linguistics: technical papers, pp 695–706

Olweus D, Limber Susan P (2018) Some problems with cyberbullying research. Curr Opin Psychol 19:139–143CrossRef

Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, Hasell J, Macdonald B, Beltekian D, Roser M (2020) Coronavirus pandemic (covid-19). Our World in Data. https://ourworldindata.org/coronavirus

Roy A, Karmakar S (2020) Bayesian semiparametric time varying model for count data to study the spread of the covid-19 cases. arXiv preprint arXiv:2004.02281, 19: 21

Signorini A, Segre A, Polgreen PM (2011) The use of twitter to track levels of disease activity and public concern in the us during the influenza a h1n1 pandemic. PloS one 6(5):e19467CrossRef

Singh S, Shaikh M, Hauck K, Miraldo M, (2021) Impacts of introducing and lifting nonpharmaceutical interventions on covid-19 daily growth rate and compliance in the united states. In: Proceedings of the National academy of sciences

Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N (2008) Cyberbullying: its nature and impact in secondary school pupils. J Child Psychol Psychiatry 49(4):376–385CrossRef

Taira K, Hosokawa R, Itatani T, Fujita S, et al (2021) Predicting the number of suicides in Japan using internet search queries: vector autoregression time series model. JMIR Public Health Surveill 7(12):e34016CrossRef

Talevi D, Socci V, Carai M, Carnaghi G, Faleri S, Trebbi E, di Bernardo A, Capelli F, Pacitti Francesca (2020) Mental health outcomes of the covid-19 pandemic. Rivista di psichiatria 55(3):137–144

Tumasjan A, Sprenger T, Sandner P, Welpe I (2010) Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Proceedings of the international AAAI conference on web and social media

Wang Q, Luo X, Ruilin T, Xiao T, Wei H (2022) Covid-19 information overload and cyber aggression during the pandemic lockdown: the mediating role of depression/anxiety and the moderating role of confucian responsibility thinking. Int J Environ Res Public Health 19(3):1540CrossRef

Wang Q, Zhou Y, Chen X, (2021) A vector autoregression prediction model for covid-19 outbreak. arXiv preprint arXiv:2102.04843,

Wiegand M, Ruppenhofer J, Schmidt A, Greenberg C (2019) Inducing a lexicon of abusive words–a feature-based approach. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, June 1-June 6, 2018, New Orleans, Louisiana, Volume 1 (Long Papers)

Yunhe W, Shi L, Que J, Lu, Qingdong Liu Lin, Lu Zhengan Xu, Yingying Liu Jiajia, Sun Y, Meng S et al (2021) The impact of quarantine on mental health status among general population in china during the covid-19 pandemic. Mol Psychiatry 26(9):4813–4822CrossRef

Zampieri M, Malmasi S, Nakov P, Rosenthal S, Farra N, Kumar R (2019) Semeval-2019 task 6: identifying and categorizing offensive language in social media (offenseval). arXiv preprint arXiv:1903.08983

Titel: An NLP-assisted Bayesian time-series analysis for prevalence of Twitter cyberbullying during the COVID-19 pandemic
verfasst von: Christopher Perez
Sayar Karmakar
Publikationsdatum: 01.12.2023
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2023
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-023-01053-4

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