Learning Semantic Relationships between Entities in Twitter

Celik, Ilknur; Abel, Fabian; Houben, Geert‐Jan

doi:10.1007/978-3-642-22233-7_12

Cited by 22 publications

(17 citation statements)

References 13 publications

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“…In Section 4.1 several studies ( [29], [30], [32], and [62]) utilize semantic technologies and related protocols to provide expanded query suggestions or to represent user preferences and similarities. Specifically, the authors of [29] proposed a framework for enriching Twitter messages with semantic relationships by analyzing Twitter posts. These relationships are identified among persons, products, and events and are utilized in order to provide query suggestion to the users.…”

Section: User-oriented Matchingmentioning

confidence: 99%

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: User-oriented Matchingmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

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“…The DBpedia knowledgebase has been used to enrich content of text, and the approach developed in [9] discovers relations between entities mentioned in user's Tweets by mapping entities to their corresponding DBpedia resources. In that approach, a pair was considered to be related if one entity is mentioned or referenced in one of the other's set of properties used in DBpedia to define a term such as full text description.…”

Section: Related Workmentioning

confidence: 99%

Inferring dynamic taxonomies for terms based on UGC

Alfayez

Joy

2013

Third International Conference on Innovative Computing Technology (INTECH 2013)

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Users of the web are currently the main content authors. Social networks form a valuable source of large volumes of user-generated content that might be beneficial in different areas of research. In this paper, we exploit the data generated by users of micro-blogging services, in particular Twitter, for disambiguating terms and inferring possible taxonomies for terms. We conducted an exploratory study to test the possibility of inferring high-level categories and possible sub-categories for which terms might be included. This experiment exploits the collection of hashtags which are mentioned with a specific hashtag in Tweets text., and the Open Directory Project (ODP), in order to discover dynamic taxonomies for a term (hashtag) with no knowledge needed about the semantic meaning of that term. We present several experiments which we have used to test this method, and promising results are reported.

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“…Solution proposed in [3], identify semantic links between persons, products, events and other entities from Twitter based on entities topics and their types according to time axis.…”

Section: Related Workmentioning

confidence: 99%