Developing a New Classifier for Automated Identification of Incivility in Social Media

Abstract

Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, the platforms themselves, and the society at large. Given the prevalence and effects of online incivility, and the challenges involved in humanbased incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments. This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments. The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter. Our best performing model achieves an F 1 of 0.802 on our Reddit test set. The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and -as such -ready to be used on vast volumes of online data. All trained models and annotated data are made available to the research community.

Patterns of Incivility on U.S. Congress Members' Social Media Accounts: A Comprehensive Analysis of the Influence of Platform, Post, and Person Characteristics

“…For the "incivil" category, results are less convincing but still on acceptable levels considering the heterogeneity of the concept. The one exception here is the Coe et al (2014) dataset, where our classifier performs significantly better than the Davidson et al (2020) model but still fails to reach reliable performance scores. However, it should be noted that other classifiers have struggled with this dataset before (Sadeque et al, 2019;Ozler et al, 2020) and that both the age and the domain of the comments (news website comments as opposed to social media comments) bear less resemblance to the project at hand than the other datasets.…”

“…To gauge the performance of the model, we tested it on the held back testing samples of the three datasets individually and used the model trained by Davidson et al (2020), explicitly labeled as an incivility classifier to be used "across social media platforms" (p. 95), as a benchmark. We furthermore manually labeled 2,000 randomly drawn posts and comments from the U.S. Congress Social Media Posts and Comments sample described in the previous section to test the performance of the model for this project specifically 3 .…”

“…Incivility classifier evaluation.BERT refers to our model trained on the manually coded datasets byCoe et al (2014),Davidson et al (2020), andTheocharis et al (2020). The model byDavidson et al (2020) is used as the benchmark.…”

Over-Time Trends in Incivility on Social Media: Evidence From Political, Non-Political, and Mixed Sub-Reddits Over Eleven Years

“…We rely on the most comprehensive longitudinal dataset of Reddit comments from 2008 to 2019. 1 and a combination of computational methods (i.e., a neural, BERT-based classifier to capture incivility in an incredibly large corpus of data, see Davidson et al, 2020) and traditional statistical inference (e.g., ANOVA and student t-test) to provide a descriptive account of online incivility 1) overtime, 2) across different contexts of online discussions (i.e., political, mixed, and non-political), and 3) as influenced by external events.…”

“…Political Science | www.frontiersin.org November 2021 | Volume 3 | Article 741605comments set aside for model testing (seeDavidson et al, 2020). The final F1-score.4 for the classification model was 0.786.…”