Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/23367
Title: Identifying social actors on twitter through profile augmentation and machine learning classification
Authors: Christodoulou, Christos 
Keywords: Machine Learning;Natural Language Processing;Text Classification;Crowdsourcing;Social Actors Classification
Advisor: Djouvas, Constantinos
Issue Date: Jun-2021
Department: Department of Communication and Internet Studies
Faculty: Faculty of Communication and Media Studies
Abstract: The Web and the Online Social Network platforms (OSNs) brought a new era in politics. Introduced by Barack Obama’s election campaign in 2008, we observe a rapidly increasing election campaigns to take place on OSNs. Citizens, Academics, Journalists, Influencers, and many more actors, on the other hand, talk about politics on the online media public sphere. This interaction creates enormous and extremely valuable data to be collected and analyzed. Recent advances in Natural Language Processing, allowed the deep contextual understanding of these data, sheeting light on the question of “what” is discussed on OSNs. However, there is huge gap in the literature, and consequently on the available techniques, on approaches that would allow someone to answer the question of “who”, i.e., different groups of users, is talking on OSNs. The aim of this research is to break new ground in public opinion interpretation using Big Data, by answering the question of “who” is discussing on OSNs and Twitter in particular. This will be achieved by proposing and developing an automated process that classifies Twitter’s users into different social actors. The methodology adopted derives from the fields of Machine Learning and Natural Language processing (for tweets classification) accompanied with political communication theories (for identifying categories of social actors). Through the combination of these disciplines, this study proposes a theoretically sound approach, unlike most existing related work in the literature, where classification categories were defined in a random manner. Moreover, the proposed methodology, in contrast to other approaches in the literature, does not rely on the tweet’s content for the classification process; this can introduce bias in cases where tweets will undergo other types of analyses, e.g., sentiment analysis. Given the above, this work proposes a novel and sound approach for classifying tweets into different social actor classes.
URI: https://ktisis.cut.ac.cy/handle/10488/23367
Rights: Απαγορεύεται η δημοσίευση ή αναπαραγωγή, ηλεκτρονική ή άλλη χωρίς τη γραπτή συγκατάθεση του δημιουργού και κάτοχου των πνευματικών δικαιωμάτων.
Type: Bachelors Thesis
Affiliation: Cyprus University of Technology 
Appears in Collections:Πτυχιακές Εργασίες/ Bachelor's Degree Theses

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