Year-end Sale % OFF 
Abstract
Telegram is a new Instant Messaging application providing key features for both public and private messaging. Telegram is similar to group broadcast or micro-blogging platforms, while on the other hand, it has features of ordinary Instant Messaging applications such as WhatsApp. In this paper, investigating a real dataset crawled from Telegram, we provide several observations which can explain the information flow, business model of content providers, and social sensing aspects of Telegram. The crawled dataset which is manually labeled by six persons contains two months of public messages of selected Telegram channels. Moreover, we introduce the viral messages in instant messaging services and propose formal definition of these messages as well as deeply analyzing their characteristics and features. Detection of virality characteristics of messages in Telegram can be beneficial for both end-users and digital marketers. Consequently, we propose statistical and word embedding approaches to detect viral messages and their sentiment and message category.Our experiments indicate that the word embedding approach can significantly outperform other baseline models.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
