2019
DOI: 10.13053/cys-23-2-3192
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Organization, Bot, or Human: Towards an Efficient Twitter User Classification

Abstract: Today, through Twitter, researchers propose approaches for classifying user accounts. However, they have to face confidence challenges owing to the diversity of the types of data propagated throughout Twitter. In addition, the messages from Twitter are imprecise, very short and even written in many dialects and languages. Moreover, the majority of the related works focus on the overall user's activity, which makes them not suitable at the post-level classification. This paper presents an alternative approach f… Show more

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Cited by 5 publications
(6 citation statements)
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“…Although the accuracy of clustering is not as high as supervised methods, using the clustering techniques, the knowledge which is hidden could be found. Moreover, in some works [11], [12], it has been proved that accuracy of unsupervised methods could be enhanced to the level as that of supervised techniques in sentiment analysis area.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Although the accuracy of clustering is not as high as supervised methods, using the clustering techniques, the knowledge which is hidden could be found. Moreover, in some works [11], [12], it has been proved that accuracy of unsupervised methods could be enhanced to the level as that of supervised techniques in sentiment analysis area.…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, here inherent information from the users is attempted to be extracted i.e., a categorization is attempted to be achieved where groups that are not predefined are obtained. Several works have been done to categorize the users but a single and specific viewpoint is analyzed in most of them, such as organization detection [6], [10]- [12], bot detection [13]- [17], political orientation detection [18], and age prediction [19]. In this work, a single perspective is not considered; rather the users are clustered and analyzed in general but considering the sentiment of the users.…”
Section: Introductionmentioning
confidence: 99%
“…Twitter user accounts have been analyzed across many dimensions. Several works have been done on a single and specific perspective such as those related to the bot detection ( [Varol,17], [Ferrara,17], [Cresci,a17], [Bindu,18], [Kudugunta,18], [Morstatter,16], [Lee,11], [Wu,17], [Jain,19], [Jain,18], [Tavares,17], [Singh,18], [Stukal,17], [Cresci,b17], [Daouadi,b19], [Cresci,15], [Kantepe,17], [Gilani,17], and [Chen,15]) and those related to the effects that bots have ( [Cresci,19], [Mazza,19], [Gilani,19] and [Cresci,18]). The first part of this section discusses the ground truth acquisition methods for bot detection.…”
Section: Related Workmentioning
confidence: 99%
“…This yielded AUC result of 92.0% using Random Forest algorithm. In [Daouadi,b19], the authors used statistical parameters extracted from the overall user's activity. The best experimental results are obtained with Deep Forest algorithm, this yielded Accuracy result of 97.55%.…”
Section: Statistical-based Approachesmentioning
confidence: 99%
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