Investigating toxicity changes of cross-community redditors from 2 billion posts and comments

Abstract

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the same community, across multiple communities, and over time. As for the toxicity detection performance, the BERT model achieved a 91.27% classification accuracy and an area under the receiver operating characteristic curve (AUC) score of 0.963 and outperformed several baseline machine learning and neural network models. The user behavior toxicity analysis showed that 16.11% of users publish toxic posts, and 13.28% of users publish toxic comments. However, results showed that 30.68% of users publishing posts and 81.67% of users publishing comments exhibit changes in their toxicity across different communities, indicating that users adapt their behavior to the communities’ norms. Furthermore, time series analysis with the Granger causality test of the volume of links and toxicity in user content showed that toxic comments are Granger caused by links in comments.

Special issue on analysis and mining of social media data

“…As many as six articles focus on sentiment analysis for different purposes ( Smetanin, 2022 ; Pratama & Firmansyah, 2022 ; Baxi, Philip & Mago, 2022 ; Nguyen & Gokhale, 2022 ; Shamoi et al., 2022 ; Ali, Irfan & Lashari, 2023 ). Four studies focused on tackling online harms of different kinds, with studies on abusive language detection ( Almerekhi, Kwak & Jansen, 2022 ; Ramponi et al., 2022 ), suicidal ideation detection ( Baghdadi et al., 2022 ) and misinformation detection ( Obeidat et al., 2022 ). Others studied NLP techniques for social media , focused on the analysis of Twitter discourse ( Heaton et al., 2023 ), language identification ( Hidayatullah et al., 2023 ) and named entity recognition ( Fudholi et al., 2023 ).…”

“… Almerekhi, Kwak & Jansen (2022) investigated changes in online behaviour of users who publish in multiple communities on Reddit by measuring their toxicity levels. They first automatically labelled a large collection of over 87 million posts as toxic or non-toxic, which they then analysed.…”

Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

“…[10] developed a framework that supports airlines in addressing customer complaints and improving services during global events like the COVID-19 pandemic through social media sentiment analysis focusing on sarcasm detection. [11] analyzed 2 billion posts and comments from Reddit to identify toxic comments. The authors hope to bring more awareness to the online harassment problem experienced by many people nowadays and potentially prevent toxic behavior on social networks.…”

Bibliometric Analysis of Granger Causality Studies

“…Granger causality has also been accepted in the areas of business, management, accounting, and economics [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. The contribution of Granger causality has also been noticed in computer science [ 42 , 43 , 44 , 45 , 46 ] and engineering [ 47 , 48 , 49 , 50 , 51 ].…”