Social networks (SN) have gained a very important role in the dissemination of news, since they allow a greater share of news than web sites and are more timely to provide updates, publishing more updated versions of the same news on the same day. The use of a variety of communication media (or channels) stimulates the need for integration and analysis of the huge amount of information published globally. The scale and heterogeneity of these messages makes the analysis of news very challenging. This paper presents an in-progress research work: the definition of a tool for clustering news according to their topics in order to understand whether there are correlations between news published by different newspapers on the same channel or by the same newspaper on different channels. We started the implementation of a method [3] based on the Keygraph algorithm [4] in order to perform multichannel clustering of news according to their topics. In this paper, we extend the proposed method [3] by considering entities in addition to the keywords to detect topics. We argue that each event can be described by entities such as times, locations, persons, things and topics. Detecting entities in a news might improve the clustering results.

Student research abstract: A key-entity graph for clustering multichannel news / Rollo, Federica. - 128005:(2017), pp. 699-700. ((Intervento presentato al convegno 32nd Annual ACM Symposium on Applied Computing, SAC 2017 tenutosi a mar nel 2017 [10.1145/3019612.3019930].

Student research abstract: A key-entity graph for clustering multichannel news

Rollo, Federica
2017

Abstract

Social networks (SN) have gained a very important role in the dissemination of news, since they allow a greater share of news than web sites and are more timely to provide updates, publishing more updated versions of the same news on the same day. The use of a variety of communication media (or channels) stimulates the need for integration and analysis of the huge amount of information published globally. The scale and heterogeneity of these messages makes the analysis of news very challenging. This paper presents an in-progress research work: the definition of a tool for clustering news according to their topics in order to understand whether there are correlations between news published by different newspapers on the same channel or by the same newspaper on different channels. We started the implementation of a method [3] based on the Keygraph algorithm [4] in order to perform multichannel clustering of news according to their topics. In this paper, we extend the proposed method [3] by considering entities in addition to the keywords to detect topics. We argue that each event can be described by entities such as times, locations, persons, things and topics. Detecting entities in a news might improve the clustering results.
32nd Annual ACM Symposium on Applied Computing, SAC 2017
mar
2017
128005
699
700
Rollo, Federica
Student research abstract: A key-entity graph for clustering multichannel news / Rollo, Federica. - 128005:(2017), pp. 699-700. ((Intervento presentato al convegno 32nd Annual ACM Symposium on Applied Computing, SAC 2017 tenutosi a mar nel 2017 [10.1145/3019612.3019930].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1170143
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