A Framework for High-Level Event Detection in a Social Network Context Via an Extension of ISEQL

Persia, Fabio; Helmer, Sven

doi:10.1109/icsc.2018.00028

Cited by 11 publications

(2 citation statements)

References 15 publications

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“…However, SI only considers the temporal distribution of tag usage. To extend this method, literature [18] considers both the temporal and geospatial distribution of tag appearances.Moreover, they apply wavelet transformation to suppress noise in user-annotated tag data and provide a multi-resolution analysis of tag usage distribution. Subsequently, literature [19] presents a novel event detection evolution model to capture the dynamic and evolving behavior of events.…”

Section: A Combination Methods Based On Wt and Ldamentioning

confidence: 99%

A Review of Research on Social Network Event Detection Methods

Chen

2024

IJCSIT

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In recent years, with the development of technological infrastructure and the use of tech products, internet usage has become widespread globally. Significant advancements have been made in the use of social networks, which are now more readily accessible through Internet and Web 3.0 technologies such as Facebook, Twitter, and Instagram. Consequently, over the past decade, numerous researchers have been developing methods for event detection based on data collected from social media platforms. The methodologies devised for discovering events are typically modular in design and novel in terms of scale and speed. To review the research in this field, we have compiled existing works on social network event detection and conducted a comprehensive and in-depth survey. Methods for social network event detection are elaborated and categorized, with performance evaluations conducted using relevant metrics. Finally, we offer perspectives on future directions.

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Section: A Combination Methods Based On Wt and Ldamentioning

confidence: 99%

A Review of Research on Social Network Event Detection Methods

Chen

2024

IJCSIT

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“…For Facebook users there are several classifications such as spammer, interactive users, message sender, photo poster, like adder, and fake users [29] . Microblogs users are categorized as Ghost writers, sellers, official accounts, and end users [30] .…”

Section: User Identificationmentioning

confidence: 99%

Design of Automatic User Identification Framework in Crowdsourcing Requirements Engineering : User Mapping and System Architecture

Delima

Riastiawan

Ashari

2023

ccitj

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The requirement elicitation is the initial stage of requirement engineering where information collected from users. The process are significantly determined by the quality and quantity of information collected. The crowdsourcing is a method of information gathering from many users. The number and variety of users in the crowdsourcing are both advantages and challenges in the elicitation process. This study purposes a framework for user identification that consists of user mapping and architecture system. The identification process consists of 8 main states, start with defining context, user target and scope determination, data source determination, user data collection, data pre-processing, feature selection, data classification and user identification. The results of this study is an initial state for development of an automated tool for user identification to elicit requirement through crowdsourcing. By the framework can be generated the user classification, which can be used to apply the appropriate method for gathering information in elicitation process.

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Exploiting Recommender Systems in Collaborative Healthcare

D’Auria

Persia

2019

Pervasive Systems, Algorithms and Networks

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A Framework for High-Level Event Detection in a Social Network Context Via an Extension of ISEQL

Cited by 11 publications

References 15 publications

A Review of Research on Social Network Event Detection Methods

A Review of Research on Social Network Event Detection Methods

Design of Automatic User Identification Framework in Crowdsourcing Requirements Engineering : User Mapping and System Architecture

Exploiting Recommender Systems in Collaborative Healthcare

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