From: Detection of fickle trolls in large-scale online social networks
Ref. | Dataset | Technique | Limitation(s) |
---|---|---|---|
[4] | Troll vulnerability metrics to predict a post is likely to become the victim of a troll attack. | Focuses on the contents of posts and the activity history of users; does not consider trolling behaviour directly. | |
[5] | Takes Holistic approach, i.e., it considers various features such as sentiment analysis, time and frequency of action and etc. | The approach is slow since it considers a magnitude of features also it suffers from false positive detection | |
[30] | Multi feature analysis, i.e., it considers the timing of tweets and the contents | It only focuses on the dataset, e.g., the usage of formal tone in trolls instead of slang and slurs | |
[31] | Classification based on multiple behavioural and content-based features such as wording and hashtags or mentions | It suffers from high false positive and only concentrates on the behaviours extracted from one specific dataset | |
[32] | Classification based on bot detection using Botometer and geolocation data | Inaccuracy of Botometer and the ability of trolls and bots to mask their real location |