Name
Abstract | ||
|---|---|---|
This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users’ tendency towards racism or sexism. This data is fed as input to the above classifiers along with the word frequency vectors derived from the textual content. We evaluate our approach on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state-of-the-art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10489-018-1242-y | Appl. Intell. |
Keywords | Field | DocType |
Text classification,Micro-blogging,Hate-speech,Deep learning,Recurrent neural networks,Twitter | Social media,Word lists by frequency,Computer science,Voice activity detection,Microblogging,Recurrent neural network,Artificial intelligence,Natural language processing,Deep learning,Machine learning | Journal |
Volume | Issue | ISSN |
48 | 12 | Applied Intelligence, 48(12), 4730-4742 (2018) |
Citations | PageRank | References |
4 | 0.46 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgios Pitsilis | 1 | 84 | 8.30 |
| Heri Ramampiaro | 2 | 154 | 20.46 |
| Helge Langseth | 3 | 8 | 1.54 |