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Future Internet
Special Issues
Social Networks Analysis and Mining
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Social Networks Analysis and Mining

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 April 2021) | Viewed by 19855

Special Issue Editor

Prof. Dr. Mehrdad Jalali

E-Mail Website
Guest Editor
Karlsruhe Institute of Technology, Karlsruhe, Germany
Interests: sentiment analysis in social networks; new social network applications; community detections algorithms; point-of-interest recommendation systems; semantic social networks; deep learning in social networks analysis

Special Issue Information

Dear Colleagues,

In recent years, social network technologies have aroused widespread attention in academia and industry. There are a wealth of data mining methods to analyze the rich structural types and related information about social networks. Today, social data can be easily obtained, which makes it easier for us to analyze and study online social networks. This new availability of big data has allowed researchers to investigate the suitability of big data mining and machine learning for the development of intelligent innovations in online social networks. Common applications include graph mining, information diffusion through SN, recommendation systems in SN, point-of-interest recommendation systems, community and hidden community detection in SN, user behavior prediction, deep learning for SNA, social content analysis, natural language processing in social networks, pattern analysis, sentiment analysis, expert finding, and security and privacy in social networks. All these topics could be potential research areas in the field of social networks and mining and are still particularly interesting topics for researchers.

Prof. Dr. Mehrdad Jalali
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Social graph mining
  • Recommendation systems in SN
  • POI recommender systems
  • Natural language processing in SN
  • Deep learning in SNA
  • Sentiment analysis
  • Security and privacy in social networks

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Published Papers (4 papers)

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Research

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15 pages, 1974 KiB  
Article
Reducing Videoconferencing Fatigue through Facial Emotion Recognition
by Jannik Rößler, Jiachen Sun and Peter Gloor
Future Internet 2021, 13(5), 126; https://doi.org/10.3390/fi13050126 - 12 May 2021
Cited by 21 | Viewed by 4488
Abstract
In the last 14 months, COVID-19 made face-to-face meetings impossible and this has led to rapid growth in videoconferencing. As highly social creatures, humans strive for direct interpersonal interaction, which means that in most of these video meetings the webcam is switched on [...] Read more.
In the last 14 months, COVID-19 made face-to-face meetings impossible and this has led to rapid growth in videoconferencing. As highly social creatures, humans strive for direct interpersonal interaction, which means that in most of these video meetings the webcam is switched on and people are “looking each other in the eyes”. However, it is far from clear what the psychological consequences of this shift to virtual face-to-face communication are and if there are methods to alleviate “videoconferencing fatigue”. We have studied the influence of emotions of meeting participants on the perceived outcome of video meetings. Our experimental setting consisted of 35 participants collaborating in eight teams over Zoom in a one semester course on Collaborative Innovation Networks in bi-weekly video meetings, where each team presented its progress. Emotion was tracked through Zoom face video snapshots using facial emotion recognition that recognized six emotions (happy, sad, fear, anger, neutral, and surprise). Our dependent variable was a score given after each presentation by all participants except the presenter. We found that the happier the speaker is, the happier and less neutral the audience is. More importantly, we found that the presentations that triggered wide swings in “fear” and “joy” among the participants are correlated with a higher rating. Our findings provide valuable input for online video presenters on how to conduct better and less tiring meetings; this will lead to a decrease in “videoconferencing fatigue”. Full article
(This article belongs to the Special Issue Social Networks Analysis and Mining)
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14 pages, 3246 KiB  
Article
RecPOID: POI Recommendation with Friendship Aware and Deep CNN
by Sadaf Safavi and Mehrdad Jalali
Future Internet 2021, 13(3), 79; https://doi.org/10.3390/fi13030079 - 22 Mar 2021
Cited by 19 | Viewed by 3033
Abstract
In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate [...] Read more.
In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches. Full article
(This article belongs to the Special Issue Social Networks Analysis and Mining)
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20 pages, 1218 KiB  
Article
Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
by Quintino Francesco Lotito, Davide Zanella and Paolo Casari
Future Internet 2021, 13(3), 76; https://doi.org/10.3390/fi13030076 - 17 Mar 2021
Cited by 13 | Viewed by 5925
Abstract
The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. [...] Read more.
The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading. Full article
(This article belongs to the Special Issue Social Networks Analysis and Mining)
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Review

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19 pages, 418 KiB  
Review
Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
by Areej Alsini, Du Q. Huynh and Amitava Datta
Future Internet 2021, 13(5), 129; https://doi.org/10.3390/fi13050129 - 14 May 2021
Cited by 11 | Viewed by 5146
Abstract
Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The [...] Read more.
Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on hashtag recommendation for tweets and present insights from previous research papers. In this paper, we search for articles related to our research between 2010 and 2020 from CiteSeer, IEEE Xplore, Springer and ACM digital libraries. From the 61 articles included in this study, we notice that most of the research papers were focused on the textual content of tweets instead of other data. Furthermore, collaborative filtering methods are seldom used solely in hashtag recommendation. Taking this perspective, we present a taxonomy of hashtag recommendation based on the research methodologies that have been used. We provide a critical review of each of the classes in the taxonomy. We also discuss the challenges remaining in the field and outline future research directions in this area of study. Full article
(This article belongs to the Special Issue Social Networks Analysis and Mining)
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