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Social Network Analysis and Mining

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01.12.2020 | Original Paper

Channel retrieval: finding relevant broadcasters on Telegram

verfasst von: Asal Jalilvand, Mahmood Neshati

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

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Abstract

Mobile broadband (3G and 4G) has remarkably influenced people’s lives. For instance, the impacts of this technology are evident in transportation, education and messaging. With the advent of this technology, a new generation of messengers offering instant messaging is available to people. One of them is Telegram that comes with new features; one of them is broadcasting messages under the name of “channel.” In this paper, we introduce the channel retrieval problem which aims to find a sorted list of related channels to a user query. This problem is first modeled to the classic information retrieval problems (expert finding and blog retrieval), but since there’s a vocabulary gap between the user query and the published messages in the channels, two query expansion methods for enhancing the performance are proposed. In this paper, a dataset is generated for the channel retrieval, which is publicly available for other researchers. Our experiments on this dataset show that using a semantic approach for query expansion can enhance channel retrieval performance.

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Titel: Channel retrieval: finding relevant broadcasters on Telegram
verfasst von: Asal Jalilvand
Mahmood Neshati
Publikationsdatum: 01.12.2020
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-020-0629-z

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