Discovering similar Twitter accounts using semantics

Razis, Gerasimos; Anagnostopoulos, Ioannis

doi:10.1016/j.engappai.2016.01.015

Cited by 21 publications

(6 citation statements)

References 8 publications

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“…• Social Matching: In [113], we proposed a methodology towards the discovery and suggestion of similar Twitter accounts. We consider term matching of user-generated content for all possible Twitter entities that may be used (mentions, replies, hashtags, URLs) according to [112] and [114].…”

Section: • Information Flow and Influence: Propagation-oriented And Dmentioning

confidence: 99%

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Modeling Influence with Semantics in Social Networks

2020

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The discovery of influential entities in all kinds of networks (e.g. social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands or products through viral content. In this work, we present a systematic review across i) online social influence metrics, properties, and applications and ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.

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Section: • Information Flow and Influence: Propagation-oriented And Dmentioning

confidence: 99%

“…Another study on the discovery and suggestion of similar Twitter users is described in [113]. It is based entirely on their disseminated content in terms of Twitter entities used (mentions, replies, hashtags, URLs).…”

Section: User-oriented Matchingmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

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“…The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter. They also introduce in [22] a methodology for discovering and suggesting similar Twitter accounts, based entirely on their disseminated content in terms of used Twitter entities (mentions, replies, hashtags, URLs). The methodology is based on semantic representation protocols and related technologies.…”

Section: Related Workmentioning

confidence: 99%

Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL

Ghawi

2020

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Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks among entities. This enables the application of de-facto techniques from Social Network Analysis (SNA) to study social relations and interactions among entities, providing deep insights into their latent social structure.

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“…Twitter has more than 300 million users and produces 6000 tweets per second [4]. Nowadays evaluating the tweets is mainly used to identify patterns and find the hidden inference of data [5].…”

Section: Introductionmentioning

confidence: 99%

An extensive work on comparing sentiment patterns in twitter archives between two persons

Sundravadivelu

Thangaraj

Gnanambal³

2022

ijhs

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In social media starting from 2006 Twitter gets Major attention because of the text shared via Twitter causes positive or negative impacts. Hence comparing two persons twitter archives is important to analyse the similarity of words they use semantics of the reviews and finding the Trends of words. This study evaluates how the characteristics of tweets are changing over years. This study finds which words used in tweets cause a number of retweets and also returns the sentiments of tweets.

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Discovering similar Twitter accounts using semantics

Cited by 21 publications

References 8 publications

Modeling Influence with Semantics in Social Networks

Modeling Influence with Semantics in Social Networks

Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL

An extensive work on comparing sentiment patterns in twitter archives between two persons

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