A Content-Based Approach to Social Network Analysis: A Case Study on Research Communities

Nart, Dario De; Degl’Innocenti, Dante; Basaldella, Marco; Agosti, Maristella; Tasso, Carlo

doi:10.1007/978-3-319-41938-1_15

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The use of ontology in the social media has been applied widely to infer semantic data in a broad range of applications. De Nart et al [26] propose a content-based approach to extract the main topics from the tweets. This approach is an attempt to understand the research communities' activities and their emerging trends.…”

Section: Sbd Incorporating Semantic Analysismentioning

confidence: 99%

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Abu-Salih

Bremie

Wongthongtham

et al. 2019

Advances in Intelligent Systems and Computing

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The wealth of Social Big Data(SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application's domain. This paper lays the theoretical background by introducing the state-ofthe-art literature review of the research topic. This is associated with a critical evaluation of the current approaches, and fortified with certain recommendations indicated to bridge the research gap.

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Section: Sbd Incorporating Semantic Analysismentioning

confidence: 99%

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Abu-Salih

Bremie

Wongthongtham

et al. 2019

Advances in Intelligent Systems and Computing

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“…In other words, incorporating semantic analysis ushers a better understanding of the contextual content of social media data through extracting their semantic data. De Nart et al (2015) proposed a content-based approach to extract the main topics from the tweets. This approach is an attempt to understand the research communities' activities and their emerging trends.…”

Section: Semantic Data Analysismentioning

confidence: 99%

Twitter mining for ontology-based domain discovery incorporating machine learning

Abu-Salih

Wongthongtham

Chan

2018

JKM

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Purpose This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs. Design/methodology/approach This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets. Findings Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics. Research limitations/implications This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data. Practical implications This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc. Originality/value This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

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“…An analysis of the papers of the first ten editions of the conference has been conducted by the researchers of the Artificial Intelligence Lab of the Department of Mathematics and Computer Science of the University of Udine. This analysis gives an account of the authors network, the most commonly presented concepts, and the network of concepts, as well as a chronological analysis of the topics of all the papers collected in the volumes of the first ten editions of IRCDL; the analysis is reported in [4].…”

Section: The Organizationmentioning

confidence: 99%

10 Years of IRCDL: 2005–2014 (Invited Paper)

Agosti

2016

Communications in Computer and Information Science

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A Content-Based Approach to Social Network Analysis: A Case Study on Research Communities

Cited by 6 publications

References 18 publications

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Twitter mining for ontology-based domain discovery incorporating machine learning

10 Years of IRCDL: 2005–2014 (Invited Paper)

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