T-PCCE: Twitter Personality based Communicative Communities Extraction System for Big Data

Kafeza, Eleanna; Kanavos, Andreas; Makris, Christos; Pispirigos, Georgios; Vikatos, Pantelis

doi:10.1109/tkde.2019.2906197

Cited by 26 publications

(6 citation statements)

References 32 publications

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“…Researchers have considered algorithms with different approaches based on the concept of modularity. In complex networks, some of these algorithms show low performance while regarding other algorithms, prior knowledge of the network is required [3,12,14,30]. Also, in [13,14,15], the concept of influence from the side of users to the side of networks is expanded and personality has been utilized as the key characteristic for identifying influential networks.…”

Section: Related Workmentioning

confidence: 99%

“…In complex networks, some of these algorithms show low performance while regarding other algorithms, prior knowledge of the network is required [3,12,14,30]. Also, in [13,14,15], the concept of influence from the side of users to the side of networks is expanded and personality has been utilized as the key characteristic for identifying influential networks. The result is to create this type of communities in Twitter graphs using a modularity-based community detection algorithm, taking into account users' personalities.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks

Kanavos

Trigka

Δρίτσας

et al. 2021

Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops

Self Cite

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In social network analysis, it is crucial to discover a community through the retrospective decomposition of a large social graph into easily interpretable subgraphs. Four major community discovery algorithms, namely the Breadth-First Search, the Louvain, the MaxToMin, and the Propinquity Dynamics, are implemented. Their correctness was functionally evaluated in the four most widely used graphs with vastly different characteristics and a dataset retrieved from Twitter regarding cultural and natural heritage data because this platform reflects public perception about historical events through means such as advanced storytelling in users timelines. The primary finding was that the Propinquity Dynamics algorithm outperforms the other algorithms in terms of NMI for most graphs. In contrast, this algorithm with the Louvain performs almost the same regarding modularity.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks

Kanavos

Trigka

Δρίτσας

et al. 2021

Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops

Self Cite

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“…The concept of audience overlap has a relatively long history in media studies [11], but the potential of overlap data to build a network of media was only lately uncovered [9,10,[12][13][14][15][16][17][18][19][20][21][22]. Network is a natural representation of the news media landscape.…”

Section: Related Workmentioning

confidence: 99%

“…Twitter is useful for studying the media landscape because of the large number of users, open access to data, and most importantly all interactions from both the news media and the audience recorded online [10]. So far, Twitter has become a laboratory of various studies related to the dynamics and propagation of information through social networks [11,12].…”

Section: Introductionmentioning

confidence: 99%

Network Model for Online News Media Landscape in Twitter

2019

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Today, most studies of audience networks analyze the landscape of the news media on the web. However, media ecology has been drastically reconfigured by the emergence of social media. In this study, we use Twitter follower data to build an online news media network that represents the pattern of news consumption in Twitter. This study adopted a weighted network model proposed by Mukerjee et al. and implemented the Filter Disparity Method suggested by Majó-Vázquez et al. to identify the most significant overlaps in the network. The implementation result on news media outlets data in three countries, namely Indonesia, Malaysia, and Singapore, shows that network analysis of follower overlap data can offer relevant insights about media diet and the way readers navigate various news sources available on social media.

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“…In addition, the use of big data in the analysis of online communities can encourage companies to develop strategies that are unethical with respect to user privacy (Muhammad et al 2018). Similarly, users' lack of knowledge about the power of AI, as well as uncertainty as to whether their data are collected individually or collectively, can raise important concerns among users about treatment, collection, and development of predictions related to their online behaviors (Kafeza et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

Saura

Palacios‐Marqués

Soriano

2023

Inf Syst E-Bus Manage

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In a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these affect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifier (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifier (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identified: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.

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T-PCCE: Twitter Personality based Communicative Communities Extraction System for Big Data

Cited by 26 publications

References 32 publications

Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks

Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks

Network Model for Online News Media Landscape in Twitter

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

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