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Recent Advances in Social Media Mining and Analysis
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Recent Advances in Social Media Mining and Analysis

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 18065

Special Issue Editor

Dr. Nirmalya Thakur

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
Interests: big data; data analysis; human-computer interaction; machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today’s world, social media serves as an “integral vehicle” [1] and “online community” [2] for seeking and sharing information, news, views, opinions, perspectives, ideas, awareness, comments, and experiences on various topics, such as pandemics, global affairs, current technologies, recent events, politics, family, relationships, and career opportunities, to name a few [3]. Mining and analysis of the Big Data of social media conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas in the last fifteen years [4].

This Special Issue welcomes papers presenting novel discoveries, theoretical findings, practical solutions, use-cases, analytical findings, novel applications, and results based on studying, analyzing, and interpreting the Big Data from social media platforms including, but not limited to, Twitter, Facebook, Instagram, TikTok, YouTube, Sina Weibo, and SnapChat. Specific topics could include, but are not limited to, text mining, text classification, text clustering, text categorization, topic modeling, opinion mining, sentiment analysis, aspect-based sentiment analysis, spam detection, fake news tracking, misinformation detection, and the identification of conspiracy theories on social media platforms.

Authors are invited to contribute their original and unpublished works. Both research and review papers are welcome. Research papers presenting preliminary and proof-of-concept results are also welcome. Authors may also submit extended versions of their conference papers. However, authors of such papers should make significant improvements/extensions to their conference papers, and the details of these improvements/extensions should be clearly outlined in the cover letter accompanying the paper submission.

References

  1. Katz, M.; Nandi, N. Social Media and Medical Education in the Context of the COVID-19 Pandemic: Scoping Review. JMIR Med. Educ. 2021, 7, e25892
  2. Lee, H.E.; Cho, J. Social Media Use and Well-Being in People with Physical Disabilities: Influence of SNS and Online Community Uses on Social Support, Depression, and Psychological Disposition. Health Commun. 2019, 34, 1043–1052
  3. Kavada, A. Social Media as Conversation: A Manifesto. Media Soc. 2015, 1, 205630511558079
  4. Bhalerao, A.A.; Naiknaware, B.R.; Manza, R.R.; Bagal, V.; Bawiskar, S.K. Social Media Mining Using Machine Learning Techniques as a Survey. In Advances in Computer Science Research; Atlantis Press International BV: Dordrecht, The Netherlands, 2023; pp. 874–889. ISBN 9789464631357.

Dr. Nirmalya Thakur
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. Information 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 media
  • big data
  • data mining
  • data analytics
  • data science
  • machine learning
  • artificial intelligence

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Editorial

Jump to: Research, Review

3 pages, 178 KiB  
Editorial
Social Media Mining and Analysis: A Brief Review of Recent Challenges
by Nirmalya Thakur
Information 2023, 14(9), 484; https://doi.org/10.3390/info14090484 - 31 Aug 2023
Cited by 10 | Viewed by 4669
Abstract
Social media platforms are a type of web-based applications that are built on the conceptual and technical underpinnings of Web 2 [...] Full article
(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)

Research

Jump to: Editorial, Review

26 pages, 2308 KiB  
Article
MLRec: A Machine Learning-Based Recommendation System for High School Students Context of Bangladesh
by Momotaz Begum, Mehedi Hasan Shuvo and Jia Uddin
Information 2025, 16(4), 280; https://doi.org/10.3390/info16040280 - 30 Mar 2025
Viewed by 260
Abstract
Social media and mobile devices, commonly referred to as socimedevices, have become integral to students’ daily lives, influencing both their academic performance and overall well-being. Depending on usage patterns, these technologies can positively or negatively impact students’ education. In recent years, many researchers [...] Read more.
Social media and mobile devices, commonly referred to as socimedevices, have become integral to students’ daily lives, influencing both their academic performance and overall well-being. Depending on usage patterns, these technologies can positively or negatively impact students’ education. In recent years, many researchers have introduced several models, including neural networks (NNs), machine learning (ML), and deep learning (DL), to identify the impact on student academic performance using a socimedevice. Here, we propose a comparative model named the MLRec model, where we assess how well different machine learning methods predict the dynamics of student life and provide a recommendation to society, parents, and academic advisors. Here, we have preprocessed our real dataset by various methods, which is collected from 10 schools and has 25 features totaling 275 instances from different districts of Bangladesh. After that, we applied 15 ML algorithms for training and testing. Then, we compared the algorithms using criteria such as accuracy, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R2), Explained Variance (EV), and Tweedie Deviance Score (D2). Subsequently, we selected the Extra Tree Classifier (ETC) algorithm based on its superior performance, achieving an accuracy of 86%, an MSE of 25%, and an EV of 40%. We also used Explainable AI (LIME and SHAP) techniques to visualize the root causes of social networks’ effects on students’ school performance. Our results show that using social media excessively adversely affects academic pursuits. Full article
(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)
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24 pages, 7283 KiB  
Article
Analysis of Cultural Perceptions of the Intangible Cultural Heritage of Chinese Porcelain Inlay: An Investigation Based on Social Media Data
by Yanyu Li and Yile Chen
Information 2025, 16(2), 124; https://doi.org/10.3390/info16020124 - 8 Feb 2025
Viewed by 940
Abstract
Cultural heritage is a precious treasure left to mankind by history. With the development of the times and the improvement of people’s education, more and more people are becoming aware of the importance of protecting cultural heritage. Chinese porcelain inlay is a type [...] Read more.
Cultural heritage is a precious treasure left to mankind by history. With the development of the times and the improvement of people’s education, more and more people are becoming aware of the importance of protecting cultural heritage. Chinese porcelain inlay is a type of architectural decoration born out of the specific historical, geographical, and cultural conditions of Fujian and Guangdong, and was included in the second batch of The National List of Intangible Cultural Heritage of China published in 2008 and the third batch of The National List of Intangible Cultural Heritage of China—Expanded Projects in 2011. It represents an important part of the complex traditional culture of Fujian and Guangdong, acting as the essence of national culture, a symbol of national wisdom, and the refinement of national spirit. Using targeted analysis and making changes based on negative reviews, organizations that protect cultural heritage can improve their actions and find new ways to spread cultural heritage. The craft of Chinese porcelain inlay is used as an example in this paper. It combines Python Octopus crawler technology, data analysis, and sentiment analysis methods to perform a cognitive social media visualization analysis of Chinese porcelain inlay, which is a form of national intangible cultural heritage in China. Then, by looking at network text data from social media, it seeks to find out how the Chinese porcelain inlay culture is passed down, what its main traits are, and how people feel about it. Finally, this study summarizes the public’s understanding of inlay porcelain and proposes strategies to promote its future development and dissemination. This study found that (1) as a form of national intangible cultural heritage in China and a unique traditional architectural decoration craft, Chinese porcelain inlay has widely recognized cultural and artistic value. (2) The emotional evaluation of Chinese porcelain inlay is mainly positive (73 and 60.76%), while negative evaluations account for 12.62 and 20.79% of responses, mainly reflected in regret regarding the gradual disappearance of old buildings, the lament that Chinese porcelain inlay is highly regional and difficult to popularize, the regret that the individual has not visited locations with Chinese porcelain inlay, a feeling of helplessness with regard to inconvenient transportation links to these places, and discontent with the prohibitively high prices of Chinese porcelain inlay products. These findings offer valuable guidance for the future dissemination and development of Chinese porcelain inlay as a form of intangible cultural heritage. (3) The LDA topic model is used to divide the perception of Chinese porcelain inlay into nine major themes: arts and crafts, leisure and entertainment, cultural travel, online appreciation, heritage protection, dissemination scope, prayer and blessing, inheritance and innovation, and collection and research. This also provides a reference for the future direction of the inheritance of Chinese porcelain inlay cultural heritage. Full article
(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)
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20 pages, 2640 KiB  
Article
Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism
by Wael M. S. Yafooz
Information 2024, 15(6), 316; https://doi.org/10.3390/info15060316 - 28 May 2024
Cited by 4 | Viewed by 1929
Abstract
Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of [...] Read more.
Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation. Full article
(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)
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44 pages, 7889 KiB  
Article
Mapping the Landscape of Misinformation Detection: A Bibliometric Approach
by Andra Sandu, Ioana Ioanăș, Camelia Delcea, Laura-Mădălina Geantă and Liviu-Adrian Cotfas
Information 2024, 15(1), 60; https://doi.org/10.3390/info15010060 - 19 Jan 2024
Cited by 14 | Viewed by 5191
Abstract
The proliferation of misinformation presents a significant challenge in today’s information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the [...] Read more.
The proliferation of misinformation presents a significant challenge in today’s information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the intent to cause harm. In some instances, individuals unwittingly share misinformation, driven by a desire to assist others without thorough research. However, there are also situations where misinformation involves negligence, or even intentional manipulation, with the aim of shaping the opinions and decisions of the target audience. Another key factor contributing to misinformation is its alignment with individual beliefs and emotions. This alignment magnifies the impact and influence of misinformation, as people tend to seek information that reinforces their existing beliefs. As a starting point, some 56 papers containing ‘misinformation detection’ in the title, abstract, or keywords, marked as “articles”, written in English, published between 2016 and 2022, were extracted from the Web of Science platform and further analyzed using Biblioshiny. This bibliometric study aims to offer a comprehensive perspective on the field of misinformation detection by examining its evolution and identifying emerging trends, influential authors, collaborative networks, highly cited articles, key terms, institutional affiliations, themes, and other relevant factors. Additionally, the study reviews the most cited papers and provides an overview of all selected papers in the dataset, shedding light on methods employed to counter misinformation and the primary research areas where misinformation detection has been explored, including sources such as online social networks, communities, and news platforms. Recent events related to health issues stemming from the COVID-19 pandemic have heightened interest within the research community regarding misinformation detection, a statistic which is also supported by the fact that half of the papers included in top 10 papers based on number of citations have addressed this subject. The insights derived from this analysis contribute valuable knowledge to address the issue, enhancing our understanding of the field’s dynamics and aiding in the development of effective strategies to detect and mitigate the impact of misinformation. The results spotlight that IEEE Access occupies the first position in the current analysis based on the number of published papers, the King Saud University is listed as the top contributor for the misinformation detection, while in terms of countries, the top-5 list based on the highest contribution to this area is made by the USA, India, China, Spain, and the UK. Moreover, the study supports the promotion of verified and reliable sources of data, fostering a more informed and trustworthy information environment. Full article
(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)
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Review

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13 pages, 1337 KiB  
Review
A Neoteric Approach toward Social Media in Public Health Informatics: A Narrative Review of Current Trends and Future Directions
by Asma Tahir Awan, Ana Daniela Gonzalez and Manoj Sharma
Information 2024, 15(5), 276; https://doi.org/10.3390/info15050276 - 13 May 2024
Viewed by 3095
Abstract
Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today’s culture, it is necessary to understand its current trends and future [...] Read more.
Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today’s culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. This review highlights the substantial influence of social media in advancing public health objectives. The key themes explored encompass the utilization of social media to advance health initiatives, monitor diseases, track behaviors, and interact with communities. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings show how social media has been used as a tool for research, implementing health campaigns, and health promotion. Social media integration with artificial intelligence (AI) and Generative Pre-Trained Transformers (GPTs) can impact and offer an innovative approach to tackle the problems and difficulties in health informatics. The research shows how social media will keep growing and evolving and, if used effectively, has the potential to help close public health gaps across different cultures and improve population health. Full article
(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)
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