Συλλογή δεδομένων από WhatsApp και Telegram

Λήτος, Γιώργης

Data crawling on WhatsApp and Telegram

Master Thesis

Author

Λήτος, Γιώργης

Date

2020-07-01

Abstract

Text Message Apps have dramatically changed the way people communicate and interact. However, the plethora of information and the ease of sharing it hinder serious problems in various areas of our daily life, given that the content of the messages may refer to fake news, illegal material and other types of illegal or immoral activities. This generates the need to timely and accurately identify messages that may contain illegal material, especially in mass media communication channels. Yet, analysis of the data communicated through these services with the objective to identify the topic of discussion in the different groups or channels is challenging, as messages are typically small in size and may contain multimedia. An equally important challenge is to deter and prevent the breach of the terms of use of these services by malicious users, wich also requires content analysis against the terms. The goal of this thesis is to assist in the direction of timely identifying messages on social media channels that may be indicative of unfair actions or breach of terms, through a primary collective analysis of messages. Specifically, we focus on data collection and analysis in two of the most popular services in the field of Text Message Apps, namely WhatsApp and Telegram. We approach the problem in distinct phases. First, we create two unique datasets fro each application, derived from conversations among members of different groups within each application. Second, we analyse this data using the K-means algorithm as well as the LDA probability distribution, aiming to identify keywords that will help us decipher key discussion topics within the groups. Next, for each cluster or topic respectively we depict the terms that seem to be most popular using Word Clouds. Finally, for each message on the channel, we calculate the probability that this message belongs to one of a specific cluster / topic, and for each channel we return the distribution of messages to the respective clusters or topics.

Postgraduate Studies Programme

Πληροφορική

Department

Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Πληροφορικής

Number of pages

154

Language

Greek

URI

http://dione.lib.unipi.gr/xmlui/handle/unipi/12828

Collections

Τμήμα Πληροφορικής

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