Detecting automatically-generated Arabic tweets

Abstract

Recently, Twitter, one of the most widely-known social media platforms, got infiltrated by several automation programs, commonly known as "bots". Bots can be easily abused to spread spam and hinder information extraction applications by posting lots of automatically-generated tweets that occupy a good portion of the continuous stream of tweets. This problem heavily affects users in the Arab region due to the recent developing political events as automated tweets can disturb communication and waste time needed in filtering such tweets. To mitigate this problem, this research work addresses the classification of Arabic tweets into automated or manual. We proposed four categories of features including formality, structural, tweet-specific, and temporal features. Our experimental evaluation over about 3.5 k randomly sampled Arabic tweets shows that classification based on individual categories of features outperform the baseline unigram-based classifier in terms of classification accuracy. Additionally, combining tweet-specific and unigram features improved classification accuracy to 92%, which is a significant improvement over the baseline classifier, constituting a very strong reference baseline for future studies.