Semantic query suggestion using Twitter Entities

Anagnostopoulos, Ioannis; Razis, Gerasimos; Mylonas, Phivos; Anagnostopoulos, Christos‐Nikolaos

doi:10.1016/j.neucom.2014.12.090

Cited by 10 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study in the area of query expansion with respect to a user's input query is introduced in [115] and [116]. Specifically, the authors proposed an algorithmic approach that can create a dynamic query suggestion set, which consists of the most viral and trendy Twitter entities (hashtags, user mentions and URLs) when considering the initial input query.…”

Section: User-oriented Matchingmentioning

confidence: 99%

“…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]. Finally, we contributed in the field of users' query expansion in [115] and [116]. Specifically, we propose an algorithmic approach, which expands a user's query by creating a suggestion set of the most viral and up-to-date Twitter entities (e.g.…”

Section: Our Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Influence with Semantics in Social Networks

2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: User-oriented Matchingmentioning

confidence: 99%

Section: Our Contributionsmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a proof-of-concept, they implement and evaluate a researcher profiling use case. Razis et al [20,21] propose an ontology schema towards linking semantified Twitter social analytics with the Linked Open Data cloud. The ontology is deployed over a publicly available service that measures how influential a Twitter account is by combining its social activity in Twitter.…”

Section: Related Workmentioning

confidence: 99%

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

Ghawi

2020

Information

View full text Add to dashboard Cite

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.

show abstract

“…Finally, content-based and data-driven approaches have been used for estimating a Twitter user's location (Cheng et al, 2010), as well as the interestingness in terms of diffusion and the content of the tweets (Naveed et al, 2011). In a previous work of ours (Anagnostopoulos et al, 2015), we utilize the data retrieved from Twitter in order to investigate the query suggestion provision that can be extracted from large graphs, having no prior knowledge of them. Towards this direction, an algorithmic approach is introduced for creating a dynamic query suggestion set which consists of the most viral and trendy Twitter entities (hashtags, mentions and URLs) with respect to a user's provided query input.…”

Section: Semantic Modeling and Recommendation In Twittermentioning

confidence: 99%

Discovering similar Twitter accounts using semantics

Razis

Anagnostopoulos

2016

Engineering Applications of Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities are found in the messages of two different accounts, the more similar, in terms of content or interest, they tend to be.Towards this direction, we introduce a methodology for discovering and suggesting similar Twitter accounts, based entirely on their disseminated content in terms of Twitter entities used. The methodology is based exclusively on semantic representation protocols and relatedtechnologies. An ontological schema is also described towards the semantification of the Twitter accounts and their entities.

show abstract

Semantic query suggestion using Twitter Entities

Cited by 10 publications

References 19 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

Discovering similar Twitter accounts using semantics

Contact Info

Product

Resources

About