Mean Birds: Detecting Aggression and Bullying on Twitter

By Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Athena Vakali. Web Science Conference, 2017

435 citations recorded by the source corpus.

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC.

Subfields: Artificial Intelligence, Communication, Social Psychology

View Paper: https://www.semanticscholar.org/paper/9847a2aa08288da4436628755daae12c7cc64183

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Citations in the corpus

Towards Ethics by Design in Online Abusive Content Detection (2020, methodology): “A combination of random sampling and targeted search have also been used (Chatzakou et al., 2017; Wulczyn et al., 2017; Founta et al., 2018).”
Comparative Studies of Detecting Abusive Language on Twitter (2018, background): “The major reason of the failure in abusive language detection comes from its subjectivity and context-dependent characteristics (Chatzakou et al., 2017).”
A Unified Deep Learning Architecture for Abuse Detection (2018, methodology): “Hate speech detection [2, 12, 25, 40, 42], cyberbullying identification [6, 11, 16], and the detection of abusive [7, 10, 27] or offensive language [8, 26, 43], are some of the facets of this problem.”
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior (2018, methodology): “It is often difﬁcult, even for a human, to distinguish (Chatzakou et al. 2017) what qualiﬁes as each.”
Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage (2020, background): “…repository for hate speech (Silva et al., 2016; Davidson et al., 2017; Founta et al., 2018; ElSh-erief et al., 2018), offensive hashtags (Chatzakou et al., 2017; Golbeck et al., 2017), and manually selected threads or subreddits (Gao and Huang, 2017; Hammer et al., 2019; Qian et al.,…”
DeepHate: Hate Speech Detection via Multi-Faceted Text Representations (2020, methodology): “Beyond the textual content, some studies have also utilized other meta-information from the users’ profiles and network structures (i.e., followers, mentioned, etc.) [9, 31, 36].”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “Deleted comments have been used to study potential expressions of regret by users and, as such, a change in their intent [23, 69]; this has also been reported as a common motivator even in self-deletion of non-abusive language [84].”
The Effect of Extremist Violence on Hateful Speech Online (2018, methodology): “To obtain a high-recall collection, we considered any new term that may constitute hate or counter-hate speech, using an inclusive, broad deﬁnition inspired by Silva et al. (2016) and Chatzakou et al. (2017), and expanded to also cover commentary and counter-hate speech elements.”
HABERTOR: An Efficient and Effective Deep Hatespeech Detector (2020, methodology): “To detect hatespeech, researchers developed human-crafted feature-based classifiers (Chatzakou et al., 2017; Davidson et al., 2017; Waseem and Hovy, 2016; MacAvaney et al., 2019), and proposed deep neural network architectures (Zampieri et al., 2019; Gambäck and Sikdar, 2017; Park and Fung, 2017;…”