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Semantic Networks for Social Media and Policy Insights
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Semantic Networks for Social Media and Policy Insights

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Information Applications".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 8562

Editors

Prof. Dr. James A Danowski

E-Mail Website
Guest Editor
Department of Communication, University of Illinois at Chicago, Chicago, IL 60607, USA
Interests: semantic networks; communication networks; social network analysis
Prof. Dr. George Barnett

E-Mail Website
Guest Editor
Department of Communication, University of California, Davis, Davis, CA 95616 USA
Interests: semantic networks; communication networks; social network analysis

Special Issue Information

Dear Colleagues,

Understanding the complex interplay between social media and policy-making is paramount in an era where digital platforms profoundly influence societal dynamics. Semantic networks offer a robust framework to model and analyze the intricate relationships and information flows within these digital ecosystems.​

This Special Issue seeks to explore the application of semantic network analysis in deciphering social media content to extract actionable policy insights. By leveraging methodologies from information science, data analytics, and communication studies, the issue aims to bridge the gap between unstructured social media data and structured policy-relevant information.​

Topics of interest include, but are not limited to:

  • Development and application of semantic network models to social media data.
  • Techniques for extracting and structuring information from social media for policy analysis.
  • Case studies demonstrating the impact of semantic network analysis on policy formulation and evaluation.
  • Integration of semantic networks with other analytical tools for comprehensive social media analysis.
  • Ethical considerations and challenges in using social media data for policy insights.​

By focusing on these areas, the Special Issue aligns with the Information journal's commitment to advancing research in information science and technology, particularly in the context of data, knowledge, and communication.​

Prof. Dr. James A Danowski
Prof. Dr. George Barnett
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 1800 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

  • semantic networks
  • social media
  • policy insights
  • communication networks
  • social network analysis

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

Published Papers (4 papers)

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Research

33 pages, 3739 KB  
Article
SEM-PDPL: Semantic Exposure Graphs for Privacy-Law-Informed Risk Assessment of Public Social-Media Data
by Heba Ismail
Information 2026, 17(8), 803; https://doi.org/10.3390/info17080803 - 20 Aug 2026
Viewed by 233
Abstract
Public social-media content often contains self-disclosed personal attributes that appear low-risk in isolation but become privacy-relevant when linked across posts, platform accounts, or user-level traces. Existing research has advanced privacy-sensitive content detection, de-anonymization analysis, social-media research ethics, and privacy-compliance workflows; however, limited work [...] Read more.
Public social-media content often contains self-disclosed personal attributes that appear low-risk in isolation but become privacy-relevant when linked across posts, platform accounts, or user-level traces. Existing research has advanced privacy-sensitive content detection, de-anonymization analysis, social-media research ethics, and privacy-compliance workflows; however, limited work operationalizes how personal-data disclosures combine structurally and how these structures can be translated into auditable governance actions. This paper proposes SEM-PDPL, a computational, privacy-law-informed risk-assessment framework for modeling public social-media exposure as semantic exposure graphs and mapping graph patterns to controls aligned with the United Arab Emirates Personal Data Protection Law (PDPL) and compatible with GDPR principles. SEM-PDPL combines governance scoping; a PDPL-informed disclosure taxonomy; hybrid extraction using rule-based methods; named-entity recognition; fine-tuned BERT; and schema-constrained large language model annotation, followed by graph construction at post, platform, corpus, and persona levels. The framework is evaluated on a synthetic multi-platform corpus of 1095 posts generated for 150 personas across 290 platform accounts. Results show that, within this controlled synthetic corpus, fine-tuned BERT provides the strongest extraction performance among six evaluated methods, achieving a macro-F1 of 0.975. Graph analysis shows that exposure density increases with aggregation, rising from 0.275 at post level to 1.000 at corpus level, and from 0.859 at platform level to 0.967 at persona level. Across all graph resolutions, quasi-identifiers emerge as the dominant weighted-degree and betweenness node, indicating that ordinary location, employer, school, and demographic cues often function as bridges connecting sensitive categories such as health and biometric data to identifying information. These findings indicate that, within this controlled corpus, privacy risk in public social-media data is not only attribute-based but also structurally graph-shaped. SEM-PDPL contributes an explainable and reproducible framework for identifying exposure hubs, sensitive bridges, and aggregation risks before applying masking, minimization, exclusion, retention, or review controls. The framework does not automate legal compliance; rather, it provides evidence-based decision support for privacy-aware social-media analytics. Full article
(This article belongs to the Special Issue Semantic Networks for Social Media and Policy Insights)
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22 pages, 840 KB  
Article
Patterned Radicalization: User Behavior Analysis and Antisemitic Language in QAnon Subreddits
by Noah D. Cohen, Peter Antonaros, Dana B. Weinberg, Meyer Levy and Jeffrey S. Kopstein
Information 2026, 17(2), 179; https://doi.org/10.3390/info17020179 - 10 Feb 2026
Cited by 1 | Viewed by 2372
Abstract
This study investigates how people in certain online communities engage with and adopt hateful rhetoric, specifically examining the escalation of antisemitic language in two deplatformed QAnon-related subreddits. Utilizing a lexicon of implicit and explicit antisemitic terms, the research analyzes over 1.26 million Reddit [...] Read more.
This study investigates how people in certain online communities engage with and adopt hateful rhetoric, specifically examining the escalation of antisemitic language in two deplatformed QAnon-related subreddits. Utilizing a lexicon of implicit and explicit antisemitic terms, the research analyzes over 1.26 million Reddit posts and comments. This study’s objective is to describe the process through which users become more deeply engaged with antisemitic content within hate-filled subcultures found online. Through the application of survival models, chi-square tests, and logistic regressions, the findings reveal that most users do not begin their engagement using antisemitic language and instead engage with antisemitic content before posting it on their own. Moreover, users who transition to posting antisemitic language typically commence with posts containing implicit terms, progressively transitioning from implicit to explicit hate speech. This escalation is particularly pronounced among users demonstrating higher levels of engagement and those interacting with influential community members, often referred to as hyper-posters. The results indicate increased involvement, exposure, and interaction with antisemitic language, especially with hyper-posters, significantly predict increased and more extreme antisemitic language. These findings illuminate the dynamic and socially contingent process through which users engage with and internalize antisemitic language within subcultural digital spaces. The study posits that language radicalization is a structured process shaped by both individual user behavior and the broader community’s social architecture. Full article
(This article belongs to the Special Issue Semantic Networks for Social Media and Policy Insights)
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29 pages, 1751 KB  
Article
The Structure of the Semantic Network Regarding “East Asian Cultural Capital” on Chinese Social Media Under the Framework of Cultural Development Policy
by Tianyi Tao and Han Woo Park
Information 2025, 16(8), 673; https://doi.org/10.3390/info16080673 - 7 Aug 2025
Cited by 3 | Viewed by 2652
Abstract
This study focuses on cultural and urban development policies under China’s 14th Five-Year Plan, exploring the content and semantic structure of discussions on the “East Asian Cultural Capital” project on the Weibo platform. It analyzes how national cultural development policies are reflected in [...] Read more.
This study focuses on cultural and urban development policies under China’s 14th Five-Year Plan, exploring the content and semantic structure of discussions on the “East Asian Cultural Capital” project on the Weibo platform. It analyzes how national cultural development policies are reflected in the discourse system related to the “East Asian Cultural Capital” on social media and emphasizes the guiding role of policies in the dissemination of online culture. When China announced the 14th Five-Year Plan in 2021, the strategic direction and policy framework for cultural development over the five-year period from 2021 to 2025 were clearly outlined. This study employs text mining and semantic network analysis methods to analyze user-generated content on Weibo from 2023 to 2024, aiming to understand public perception and discourse trends. Word frequency and TF-IDF analyses identify key terms and issues, while centrality and CONCOR clustering analyses reveal the semantic structure and discourse communities. MR-QAP regression is employed to compare network changes across the two years. Findings highlight that urban cultural development, heritage preservation, and regional exchange are central themes, with digital media, cultural branding, trilateral cooperation, and cultural–economic integration emerging as key factors in regional collaboration. Full article
(This article belongs to the Special Issue Semantic Networks for Social Media and Policy Insights)
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20 pages, 1925 KB  
Article
Beyond Polarity: Forecasting Consumer Sentiment with Aspect- and Topic-Conditioned Time Series Models
by Mian Usman Sattar, Raza Hasan, Sellappan Palaniappan, Salman Mahmood and Hamza Wazir Khan
Information 2025, 16(8), 670; https://doi.org/10.3390/info16080670 - 6 Aug 2025
Cited by 1 | Viewed by 2102
Abstract
Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating [...] Read more.
Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating rich contextual information from text. Using state-of-the-art transformer models on the Sentiment140 dataset, our framework extracts three concurrent signals from each tweet: sentiment polarity, aspect-based scores (e.g., ‘price’ and ‘service’), and topic embeddings. These features are aggregated into a daily multivariate time series. We then employ a SARIMAX model to forecast future sentiment, using the extracted aspect and topic data as predictive exogenous variables. Our results, validated on the historical Sentiment140 Twitter dataset, demonstrate the framework’s superior performance. The proposed multivariate model achieved a 26.6% improvement in forecasting accuracy (RMSE) over a traditional univariate ARIMA baseline. The analysis confirmed that conversational aspects like ‘service’ and ‘quality’ are statistically significant predictors of future sentiment. By leveraging the contextual drivers of conversation, the MFSF framework provides a more accurate and interpretable tool for businesses and policymakers to proactively monitor and anticipate shifts in public opinion. Full article
(This article belongs to the Special Issue Semantic Networks for Social Media and Policy Insights)
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