Skip to main content

Table 1 Comparison of various troll and bot detection approaches discussed in the literature

From: Detection of fickle trolls in large-scale online social networks

Ref. Dataset Technique Limitation(s)
[4] Reddit 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] Twitter 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] Twitter 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] Twitter 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] Twitter 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