Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection and social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams.In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze fake channels on Telegram. To automatically detect fake channels, we propose a machine learning model that achieves an accuracy of 85.49%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.
It's a Trap! Detection and Analysis of Fake Channels on Telegram / LA MORGIA, Massimo; Mei, Alessandro; Mongardini, ALBERTO MARIA; Wu, Jie. - (2023), pp. -104. (Intervento presentato al convegno 2023 IEEE International Conference on Web Services (ICWS) tenutosi a CHICAGO, ILLINOIS USA) [10.1109/icws60048.2023.00026].
It's a Trap! Detection and Analysis of Fake Channels on Telegram
Massimo La Morgia;Alessandro Mei;Alberto Maria Mongardini;
2023
Abstract
Telegram is a widely used instant messaging app that has gained popularity due to its high level of privacy protection and social network features like channels, which are virtual rooms where only administrators can post and broadcast messages to all subscribers. However, these same features have also led to the emergence of problematic activities and a significant number of fake accounts. To address these issues, Telegram has introduced verified and scam marks for channels, but only a small number of official channels are currently marked as verified, and only a few fakes as scams.In this research, we conduct a large-scale analysis of Telegram by collecting data from 120,979 different public channels and over 247 million messages. We identify and analyze fake channels on Telegram. To automatically detect fake channels, we propose a machine learning model that achieves an accuracy of 85.49%. By applying this model to our dataset, we find the main targets of fakes are political figures, well-known people such as actors or singers, and services.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.