Predicting the victims of hate speech on microblogging platforms
Sahrish Khan, Rabeeh Ayaz Abbasi, Muddassar Azam Sindhu, Sachi Arafat, Akmal Saeed Khattak, Ali Daud, Mubashar Mushtaq · 2024
Read Predicting the victims of hate speech on microblogging platforms by Sahrish Khan, Rabeeh Ayaz Abbasi, Muddassar Azam Sindhu, Sachi Arafat, Akmal Saeed Khattak, Ali Daud, Mubashar Mushtaq (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11652838.txt.
Description
Most work on hate speech online scans the posts themselves; Sahrish Khan and co-authors flip the lens in a 2024 study and ask who gets targeted. They build a data set of posts labeled by whether the user addressed was a hate target, then train models to spot likely targets in advance.
Word patterns, short word chains, and grammar tags fed several standard learning methods. One simple method, a naive Bayes sorter, hit the best score and beat its rivals by a wide edge. The team reports which feature mixes worked best for the task. A tool that flags likely targets early could let platforms warn or shield users before abuse lands, a shift from cleaning up hate to heading it off.
Catalog record
| CREATOR | Sahrish Khan, Rabeeh Ayaz Abbasi, Muddassar Azam Sindhu, Sachi Arafat, Akmal Saeed Khattak, Ali Daud, Mubashar Mushtaq |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |