Content-Based Text Classifiers for Pornographic Web Filtering

What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus

“…Compared to hate speech, the detection of sexually explicit content has received less attention from the NLP community, with existing ML approaches focusing mainly on the detection of explicit images (Wehrmann et al, 2018;Rowley et al, 2006) and URLs (Matic et al, 2020), whereas n-grambased approaches remain predominantly used in practice by web providers (Hammami et al, 2003;Polpinij et al, 2006;Ho and Watters, 2004). In our analysis, we used a list of n-grams extracted from adult websites in order to establish the percentage of websites from our sample that contained sexually explicit content; however, we found no available statistical or ML-based approach that we could use to compare our count-based approach with.…”

Analysing Twitter Data for Phishing Tweets Identification

“…As a matter of reality, several of this detection work is finished offline. Graph options adopted by Du et al (2003) like the centrality scores of network positions "betweenness" while the redirection data adopted by (Polpinij et al, 2006) is overwhelming time, creating a troublesome to the idea of studying Internet contexts as a social science. Additionally, Polpinij et al (2008) consumed abundant time once shrewd the carefulness of users' behaviours.…”

Automatic Identification of Age-Appropriate Ratings of Song Lyrics

“…There is some thematically related work, such as automatic filtering of pornographic content (Polpinij et al, 2006;Sood et al, 2012;Xiang et al, 2012;Su et al, 2004), but we believe the nature of the task is significantly different such that a different approach is required.…”