Work

Exploiting geolocation, user and temporal information for natural hazards monitoring in Twitter

Year: 2015

Type: article

Abstract: During emergency situation events it is important to acquire as much information about the event as possible, and social media sites like Twitter offer important real-time user contributed data. Typical Information Filtering techniques are keyword-based approaches or focused on co-occurrence with keywords. However, this approximations can lose relevant local information if messages do not contain an initially considered event-related keyword. Considering geolocation, user and temporal information within a pseudo-relevance feedback approach we can find event-related terminology but not co-occurring with initially considered keywords. Thus, taking into account the temporal aspect we can modify a query expansion function like Kullback-Leibler divergence in order to improve the Information Filtering process. The proposals has been evaluated in two datasets of real-world events obtaning encouraging results.

Source: Warwick Research Archive Portal (University of Warwick)

DOI: 10.82444/warw.33713803

Authors: Víctor Fresno, Arkaitz Zubiaga, Heng Ji, Raquel Martínez

Language: English

Topic: Public Relations and Crisis Communication

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funders: Ministerio de Ciencia e Innovación, Universidad Nacional de Educación a Distancia, Army Research Laboratory

Awards: G3123930367, G5259331294, G948678646

OpenAlex ID: W1579601000