Content-Driven Detection of Cyberbullying on the Instagram Social Network

By Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, S. Rajtmajer, C. Griffin, David J. Miller, Cornelia Caragea. 2016

175 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “[93] collected a dataset of Instagram posts and comments, consisting of 3000 examples.”
A Survey on Hate Speech Detection using Natural Language Processing (2017, methodology): “More recently, many authors have shifted to employing the term cyberbullying (Xu et al., 2012; Hosseinmardi et al., 2015; Zhong et al., 2016; Van Hee et al., 2015; Dadvar et al., 2013; Dinakar et al., 2012).”
Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, background): “…online harassment (Yin and Davison, 2009; Golbeck et al., 2017), abusive language (Mehdad and Tetreault, 2016; Park and Fung, 2017), cyberbullying (Dad-var et al., 2013; Dinakar et al., 2012; Hee et al., 2015; Zhong et al., 2016) and offensive language (Chen et al., 2012; Xiang et al., 2012).”
Detection of Abusive Language: the Problem of Biased Datasets (2019, background): “In the literature, closely related terms include hate speech (Waseem and Hovy, 2016) or cyber bullying (Zhong et al., 2016).”
Lifelong Learning of Hate Speech Classification on Social Media (2021, background): “Most of the previous work focuses on binary classiﬁcation (Warner and Hirschberg, 2012; Zhong et al., 2016; Nobata et al., 2016; Gao et al., 2017; Qian et al., 2018b) or coarse-grained multi-1 Figure 1: An illustration of our proposed task. hg i : the i th hate group.”
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (2018, background): “Hirschberg, 2012; Zhong et al., 2016), sentiment analysis (Van Hee et al., 2015), lexical resources and linguistic features (Burnap and Williams, 2016), knowledge-based features (Dinakar et al., 2012), meta-information (Waseem and Hovy, 2016), and multi-modal information (Zhong et al., 2016).”
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network (2018, methodology): “In addition, Knowledge-Based features such as messages mapped to stereotypical concepts in a knowledge base [8] and multimodal information such as image captions and pixel features [28] were used in cyber bully detection but only in very confined context [21].”
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification (2019, background): “Zhong et al. (2016) also studied the combination of image and captions for the task of detecting cyberbullying.”
Exploring Hate Speech Detection in Multimodal Publications (2019, background): “[30] worked on classifying Instagram images as potential cyberbullying targets, exploiting both the image content, the image caption and the comments.”
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality (2022, background): “…implausible scenes [13], exploitation of language and vision priors [11, 27], single word mismatches [64], hate speech detection [26, 32, 41, 92], memes [39, 75], ablation of one modality to probe the other [22], distracting models with visual similarity between images [7, 33], distracting…”
Explaining Toxic Text via Knowledge Enhanced Text Generation (2022, background): “Early approaches include using n-grams (Waseem and Hovy, 2016; Sood et al., 2012; Perera and Fer-nando, 2021) as well as word clustering (Xiang et al., 2012; Zhong et al., 2016).”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “…use supervised-approaches to tackle hate speech detection on SMPs including blogs (Warner and Hirschberg, 2012; Djuric et al., 2015; Gitari et al., 2015), Twitter (Xiang et al., 2012; Silva et al., 2016; Mathew et al., 2018a), Face-book (Del Vigna et al., 2017), and Instagram (Zhong et al., 2016).”
Studying Generalisability across Abusive Language Detection Datasets (2019, background): “…tasks have been collected from various social media platforms, such as Twitter (Waseem and Hovy, 2016; Davidson et al., 2017; Founta et al., 2018; Burnap and Williams, 2015; Golbeck et al., 2017), Facebook (Kumar et al., 2018a), Instagram (Hos-seinmardi et al., 2015; Zhong et al., 2016), Yahoo!”
Hierarchical CVAE for Fine-Grained Hate Speech Classification (2018, background): “Gao et al. (2017), Zhong et al. (2016), and Nobata et al. (2016) differentiate between abusive or not.”
Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021, background): “In the literature, closely related terms include hate speech (Waseem and Hovy, 2016) or cyberbullying (Zhong et al., 2016).”