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Mathematics
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Soft Computing for Social Media Data Analytics
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Soft Computing for Social Media Data Analytics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8037

Special Issue Editors

Dr. Fei Hao

E-Mail Website
Guest Editor
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Interests: social computing; soft computing; big data mining
Prof. Dr. Doo-Soon Park

E-Mail Website
Guest Editor
Department of Computer Software and Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
Interests: social network analysis; data mining; big data; distribute system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carson K. Leung

E-Mail Website
Guest Editor
Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: AI; data mining and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Song

E-Mail Website
Guest Editor
College of Computer Science and Technology, North China University of Technology, Beijing 100144, China
Interests: environment perception; unmanned ground vehicle; 3D reconstruction; object recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The characteristics of massive social media data, diverse mobile sensing devices and the highly complex and dynamic users’ social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Therefore, representing and processing this massive uncertainty in social media data and providing high-quality services for users is becoming a great challenge.

Soft computing techniques (such as fuzzy logic, artificial neural networks and so forth) can play a significantly important role in this due to their inherent capabilities of dealing with imprecision and uncertainty.

This Special Issue provides a platform for researchers and practitioners from communities of artificial intelligence, data mining, mobile computing and social networks to share their ideas, innovations, research achievements and solutions in fostering the advancement of intelligent data analytics and management of social media data. We solicit original, unpublished, and innovative research work on applying any intelligent technologies and methods to all aspects around the theme of this Special Issue.

Scope and Topics:

  • Fundamentals of Social Computing
  • Fuzzy Logic, Rough Set, Formal Concept Analysis (FCA), Soft Set Theories for Social Networks Analysis
  • Modeling Social Media based on Soft Computing
  • Data mining for Social Media Data
  • Communities Mining in Social Media
  • Expert Systems for Social Media Data
  • Recommendation Systems and Marketing
  • Trust and Reputation Evaluation in (Mobile) Social Networks
  • Methods for Social Structure and Community Discovery
  • Methods for Tie Strength or Link Prediction
  • Methods for Extracting and Understanding User and Group behavior
  • Big Social Media Data
  • Soft Computing Modeling for Collective Intelligence
  • Other issues related to the Advances of Soft Computing in Various Social Computing
  • Applications and Case Studies

Dr. Fei Hao
Prof. Dr. Doo-Soon Park
Prof. Dr. Carson K. Leung
Prof. Dr. Wei Song
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • soft computing
  • social network
  • data mining
  • network science
  • computational intelligence

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Published Papers (4 papers)

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Research

22 pages, 18608 KiB  
Article
Rumor Detection in Social Media Based on Multi-Hop Graphs and Differential Time Series
by Jianhong Chen, Wenyi Zhang, Hongcai Ma and Shan Yang
Mathematics 2023, 11(16), 3461; https://doi.org/10.3390/math11163461 - 9 Aug 2023
Cited by 1 | Viewed by 1632
Abstract
The widespread dissemination of rumors (fake information) on online social media has had a detrimental impact on public opinion and the social environment. This necessitates the urgent need for efficient rumor detection methods. In recent years, deep learning techniques, including graph neural networks [...] Read more.
The widespread dissemination of rumors (fake information) on online social media has had a detrimental impact on public opinion and the social environment. This necessitates the urgent need for efficient rumor detection methods. In recent years, deep learning techniques, including graph neural networks (GNNs) and recurrent neural networks (RNNs), have been employed to capture the spatiotemporal features of rumors. However, existing research has largely overlooked the limitations of traditional GNNs based on message-passing frameworks when dealing with rumor propagation graphs. In fact, due to the issues of excessive smoothing and gradient vanishing, traditional GNNs struggle to capture the interactive information among high-order neighbors when handling deep graphs, such as those in rumor propagation scenarios. Furthermore, previous methods used for learning the temporal features of rumors, whether based on dynamic graphs or time series, have overlooked the importance of differential temporal information. To address the aforementioned issues, this paper proposes a rumor detection model based on multi-hop graphs and differential time series. Specifically, this model consists of two components: the structural feature extraction module and the temporal feature extraction module. The former utilizes a multi-hop graph and the enhanced message passing framework to learn the high-order structural features of rumor propagation graphs. The latter explicitly models the differential time series to learn the temporal features of rumors. Extensive experiments conducted on multiple real-world datasets demonstrate that our proposed model outperforms the previous state-of-the-art methods. Full article
(This article belongs to the Special Issue Soft Computing for Social Media Data Analytics)
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23 pages, 9927 KiB  
Article
Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data
by Pir Noman Ahmad, Yuanchao Liu, Gauhar Ali, Mudasir Ahmad Wani and Mohammed ElAffendi
Mathematics 2023, 11(12), 2668; https://doi.org/10.3390/math11122668 - 12 Jun 2023
Cited by 5 | Viewed by 1560
Abstract
Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making [...] Read more.
Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking. Full article
(This article belongs to the Special Issue Soft Computing for Social Media Data Analytics)
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19 pages, 515 KiB  
Article
TALI: An Update-Distribution-Aware Learned Index for Social Media Data
by Na Guo, Yaqi Wang, Haonan Jiang, Xiufeng Xia and Yu Gu
Mathematics 2022, 10(23), 4507; https://doi.org/10.3390/math10234507 - 29 Nov 2022
Cited by 2 | Viewed by 1611
Abstract
In the growing mass of social media data, how to efficiently extract the collection of interested concerns has become a research hotspot. Due to the large size and regularity of social media data, traditional indexing techniques are not applicable. Our “Learned Index”, which [...] Read more.
In the growing mass of social media data, how to efficiently extract the collection of interested concerns has become a research hotspot. Due to the large size and regularity of social media data, traditional indexing techniques are not applicable. Our “Learned Index”, which is a part of social media intelligence solutions, uses mathematical principles to summarize the laws from the data. It predicts the location of the data by learning the mathematical properties of the data distribution to build the model. Although existing methods over single dimension and multi-dimension such as setting gaps are proposed to further optimize the performance of index, they do not consider the update-distribution of data. In this paper, we propose an update-distribution-aware learned index for social media data (TALI) to support update operations and handle the data sliding. In TALI, underlying data are learned through machine learning models, and a recursive hierarchical model is built. It also learns the update-distribution of data to adjust the size of each leaf node. Thus, it can more effectively support all kinds of operations in databases due to the decrease of the leaf nodes’ sliding. In addition, TALI uses the model-based insertion method for bulkload and query, resulting in a small prediction error. Thus, exponential search is used to perform secondary lookup to improve query efficiency. Experiments were tested and compared on four realistic and synthetic social media datasets. Through extensive experiments, TALI performed better than the existing state-of-the-art learned index with less space occupancy on four realistic and synthetic social media datasets. Full article
(This article belongs to the Special Issue Soft Computing for Social Media Data Analytics)
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30 pages, 5851 KiB  
Article
Exhaustive Exploitation of Local Seeding Algorithms for Community Detection in a Unified Manner
by Yanmei Hu, Bo Yang, Bin Duo and Xing Zhu
Mathematics 2022, 10(15), 2807; https://doi.org/10.3390/math10152807 - 8 Aug 2022
Cited by 3 | Viewed by 1890
Abstract
Community detection is an essential task in network analysis and is challenging due to the rapid growth of network scales. Recently, discovering communities from the local perspective of some specified nodes called seeds, rather than requiring the global information of the entire network, [...] Read more.
Community detection is an essential task in network analysis and is challenging due to the rapid growth of network scales. Recently, discovering communities from the local perspective of some specified nodes called seeds, rather than requiring the global information of the entire network, has become an alternative approach to addressing this challenge. Some seeding algorithms have been proposed in the literature for finding seeds, but many of them require an excessive amount of effort because of the global information or intensive computation involved. In our study, we formally summarize a unified framework for local seeding by considering only the local information of each node. In particular, both popular local seeding algorithms and new ones are instantiated from this unified framework by adopting different centrality metrics. We categorize these local seeding algorithms into three classes and compare them experimentally on a number of networks. The experiments demonstrate that the degree-based algorithms usually select the fewest seeds, while the denseness-based algorithms, except the one with node mass as the centrality metric, select the most seeds; using the conductance of the egonet as the centrality metric performs best in discovering communities with good quality; the core-based algorithms perform best overall considering all the evaluation metrics; and among the core-based algorithms, the one with the Jaccard index works best. The experimental results also reveal that all the seeding algorithms perform poorly in large networks, which indicates that discovering communities in large networks is still an open problem that urgently needs to be addressed. Full article
(This article belongs to the Special Issue Soft Computing for Social Media Data Analytics)
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