Semantic Analysis of Social Data Streams

Modeling Community Behavior through Semantic Analysis of Social Data

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization - UMAP '16 ◽

10.1145/2930238.2930274 ◽

2016 ◽

Cited By ~ 1

Author(s):

Cataldo Musto ◽

Giovanni Semeraro ◽

Marco de Gemmis ◽

Pasquale Lops

Keyword(s):

Semantic Analysis ◽

Social Data ◽

Community Behavior

Download Full-text

CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor

Journal of Information Science ◽

10.1177/0165551518790424 ◽

2018 ◽

Vol 45 (2) ◽

pp. 259-280 ◽

Cited By ~ 13

Author(s):

Bilal Abu-Salih ◽

Pornpit Wongthongtham ◽

Kit Yan Chan ◽

Dengya Zhu

Keyword(s):

Semantic Analysis ◽

Social Trust ◽

Viral Marketing ◽

Social Data ◽

Fine Grained ◽

Computer Scientists ◽

Real World Datasets ◽

Information Consumers ◽

Expertise Retrieval ◽

Temporal Factor

The widespread use of big social data has influenced the research community in several significant ways. In particular, the notion of social trust has attracted a great deal of attention from information processors and computer scientists as well as information consumers and formal organisations. This attention is embodied in the various shapes social trust has taken, such as its use in recommendation systems, viral marketing and expertise retrieval. Hence, it is essential to implement frameworks that are able to temporally measure a user’s credibility in all categories of big social data. To this end, this article suggests the CredSaT (Credibility incorporating Semantic analysis and Temporal factor), which is a fine-grained credibility analysis framework for use in big social data. A novel metric that includes both new and current features, as well as the temporal factor, is harnessed to establish the credibility ranking of users. Experiments on real-world datasets demonstrate the efficacy and applicability of our model in determining highly domain-based trustworthy users. Furthermore, CredSaT may also be used to identify spammers and other anomalous users.

Download Full-text

Online Clustering for Topic Detection in Social Data Streams

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) ◽

10.1109/ictai.2016.0062 ◽

2016 ◽

Cited By ~ 2

Author(s):

Carmela Comito ◽

Clara Pizzuti ◽

Nicola Procopio

Keyword(s):

Data Streams ◽

Topic Detection ◽

Social Data ◽

Online Clustering

Download Full-text

Social Media Multidimensional Analysis for Intelligent Health Surveillance

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17072289 ◽

2020 ◽

Vol 17 (7) ◽

pp. 2289

Author(s):

María José Aramburu ◽

Rafael Berlanga ◽

Indira Lanza

Keyword(s):

Social Networks ◽

Data Streams ◽

Public Health Surveillance ◽

Quality Metrics ◽

Health Surveillance ◽

Multidimensional Analysis ◽

User Profiles ◽

Multidimensional Model ◽

Social Data

Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

Download Full-text