Detecting Nastiness in Social Media

By Niloofar Safi Samghabadi, Suraj Maharjan, Alan P. Sprague, Raquel Diaz-Sprague, Thamar Solorio. 2017

39 citations recorded by the source corpus.

Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others. There is a lack of adequate resources to detect and hinder its occurrence. In this paper, we present our initial NLP approach to detect invective posts as a first step to eventually detect and deter cyberbullying. We crawl data containing profanities and then determine whether or not it contains invective. Annotations on this data are improved iteratively by in-lab annotations and crowdsourcing. We pursue different NLP approaches containing various typical and some newer techniques to distinguish the use of swear words in a neutral way from those instances in which they are used in an insulting way. We also show that this model not only works for our data set, but also can be successfully applied to different data sets.

Subfields: Artificial Intelligence, Communication, Social Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Communication

In Social Psychology

Citations in the corpus

Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, methodology): “Toxic comment identification is a supervised classification task and approached by either methods including manual feature engineering (Burnap and Williams, 2015; Mehdad and Tetreault, 2016; Waseem, 2016; Davidson et al., 2017; Nobata et al., 2016; Kennedy et al., 2017; Samghabadi et al., 2017; Robi”
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, methodology): “Samghabadi et al. (2019) utilize an emotion-aware attention mechanism, achieving a macro-F1 of 83.”