Name
Abstract | ||
|---|---|---|
Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better results in toxic content classification. An empirical study was also conducted to analyze the topics of the insincere questions on Quora, and we found that topics on "religion", "gender" and "politics" has a higher proportion of insincere questions.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341161.3345332 | ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining
Vancouver
British Columbia
Canada
August, 2019 |
Keywords | Field | DocType |
NLP, Word Embedding, Sequence Model, Text Classification, Toxic Content | Computer science,Artificial intelligence,Natural language processing,Word embedding,Machine learning | Conference |
ISSN | ISBN | Citations |
2473-9928 | 978-1-4503-6868-1 | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Do Yeon Kim | 1 | 0 | 0.34 |
| Xiaohang Li | 2 | 0 | 0.68 |
| Sheng Wang | 3 | 85 | 7.78 |
| Yunying Zhuo | 4 | 0 | 0.34 |
| Roy Ka-Wei Lee | 5 | 16 | 7.78 |