Media matters: a mixed-method look into the research on misinformation and communication over 30 years

Computational network analysis of two popular skin cancers provides insights into the molecular mechanisms and reveals common therapeutic targets.

MYBPC1 computational phosphoprotein network construction and analysis of frontal cortex of HIV encephalitis (HIVE) was very useful to identify novel markers and potential targets for prognosis and therapy. Based on integrated gene regulatory network infer method by linear programming and a decomposition procedure with analysis of the significant function cluster using kappa statistics and fuzzy heuristic clustering from the database for annotation, visualization, and integrated discovery, we identified and constructed significant molecule MYBPC1 phosphoprotein network from 12 frontal cortex of HIVEcontrol patients and 16 HIVE in the same GEO Dataset GDS1726. Our result verified MYBPC1 phosphoprotein module only in the upstream of frontal cortex of HIVEcontrol patients (CREB5, MAPKAPK3 inhibition), whereas in the upstream of frontal cortex of HIVE (CREB5, ZC3HAV1 activation; ROR1 inhibition) and downstream (MAPKAPK3 activation; CFDP1, PDCD4, RBBP6 inhibition). Importantly, we determined that MYBPC1 phosphoprotein cluster of HIVE was involved in signal transduction, transferase, post-translational protein modification, developmental process and glycoprotein (only in HIVE terms), the condition was vital to inflammation and cognition impairment of HIVE. Our result demonstrated that common terms in both HIVE-control patients and HIVE included phosphoprotein, organelle, response to stimulus, nucleic acid binding, primary metabolic process, and biological regulation, and these terms were more relative to inflammation and cognition impairment, therefore, we deduced the stronger MYBPC1 phosphoprotein network in HIVE. It would be necessary of the stronger MYBPC1 phosphoprotein function to inflammation and cognition impairment of HIVE.

RRM2 computational phosphoprotein network construction and analysis of human hepatocellular carcinoma (HCC) is very useful to identify novel markers and potential targets for prognosis and therapy. By integration of gene regulatory network infer (GRNInfer) and the database for annotation, visualization and integrated discovery (DAVID) we identified and constructed significant molecule RRM2 phosphoprotein network from 25 no-tumor hepatitis/cirrhotic liver tissues and 25 HCC patients in the same GEO Dataset GSE10140-10141. We gained the negative result of RRM2 phosphoprotein module through the net numbers of activation minus inhibition compared with no-tumor hepatitis/cirrhotic liver tissues and predicted possibly the decrease of RRM2 phosphoprotein module in HCC. Our integrative result showed that RRM2 phosphoprotein cluster of HCC contained both in human no-tumor hepatitis/cirrhotic liver tissues and HCC terms of phosphoprotein (with RRM2) and cell cycle (without RRM2), only in HCC terms of cell-cell signaling, cell projection part, glycoprotein, cell projection, cell adhesion, biological adhesion, integral to plasma membrane, plasma membrane, kinase and phosphorus metabolic process (without RRM2), and none in HCC terms of cell death (without RRM2) and ion binding (with RRM2) compared with human no-tumor hepatitis/cirrhotic liver tissues, all the condition is vital to invasion of HCC. Therefore, we deduced the weaker RRM2 phosphoprotein function in HCC consistent with our above computation. It would be necessary of RRM2 phosphoprotein function decrease to invasion of HCC. RRM2 phosphoprotein interaction module construction in HCC can be a new route for studying the pathogenesis of HCC.

CREB5 computational regulation network construction and analysis of frontal cortex of HIV encephalitis (HIVE) is very useful to identify novel markers and potential targets for prognosis and therapy. By integration of gene regulatory network infer and the database for annotation, visualization and integrated discovery we identified and constructed significant molecule CREB5 regulation network from 12 frontal cortex of HIVE-control patients and 16 HIVE in the same GEO Dataset GDS1726. Our result verified CREB5 biological regulation module in the upstream of frontal cortex of HIVE-control patients (MAPKAPK3 activation; DGKG, LY96, TNFRSF11B inhibition) and downstream (ATP6V0E1, CFB, DGKG, MX1, TGFBR3 activation; LGALS3BP, RASGRP3, RDX, STAT1 inhibition), whereas in the upstream of frontal cortex of HIVE (BST2, CFB, LCAT, TNFRSF11B activation; CFHR1, LY96 inhibition) and downstream (GAS1, LCAT, LGALS3BP, NFAT5, VEZF1, ZNF652 activation; DGKG, IFITM1, LY96, TNFRSF11B inhibition). Importantly, we datamined that CREB5 regulation cluster of HIVE was involved in inflammatory response, proteolysis, biological adhesion, and negative regulation of biological process (only in HIVE terms) without positive regulation of cellular process, phosphotransferase, kinase, post-translational protein modification, ATP binding, transmembrane protein, calcium ion binding, acetylation, and hydrolase activity (only in HIVE-control patients terms), the condition was vital to inflammation and cognition impairment of HIVE. Our result demonstrated that common terms in both HIVE-control patients and HIVE included biological regulation, phosphoprotein, metabolic process, zinc, biosynthetic process, organelle, signal transduction, defense response, membrane, secreted, signal peptide, and glycoprotein, and these terms were more relative to inflammation and cognition impairment, therefore we deduced the stronger CREB5 regulation network in HIVE consistent with our number computation. It would be necessary of the stronger CREB5 regulation function to inflammation and cognition impairment of HIVE.

BackgroundTNFRSF11B computational development network construction and analysis of frontal cortex of HIV encephalitis (HIVE) is very useful to identify novel markers and potential targets for prognosis and therapy.MethodsBy integration of gene regulatory network infer (GRNInfer) and the database for annotation, visualization and integrated discovery (DAVID) we identified and constructed significant molecule TNFRSF11B development network from 12 frontal cortex of HIVE-control patients and 16 HIVE in the same GEO Dataset GDS1726.ResultsOur result verified TNFRSF11B developmental process only in the downstream of frontal cortex of HIVE-control patients (BST2, DGKG, GAS1, PDCD4, TGFBR3, VEZF1 inhibition), whereas in the upstream of frontal cortex of HIVE (DGKG, PDCD4 activation) and downstream (CFDP1, DGKG, GAS1, PAX6 activation; BST2, PDCD4, TGFBR3, VEZF1 inhibition). Importantly, we datamined that TNFRSF11B development cluster of HIVE is involved in T-cell mediated immunity, cell projection organization and cell motion (only in HIVE terms) without apoptosis, plasma membrane and kinase activity (only in HIVE-control patients terms), the condition is vital to inflammation, brain morphology and cognition impairment of HIVE. Our result demonstrated that common terms in both HIVE-control patients and HIVE include developmental process, signal transduction, negative regulation of cell proliferation, RNA-binding, zinc-finger, cell development, positive regulation of biological process and cell differentiation.ConclusionsWe deduced the stronger TNFRSF11B development network in HIVE consistent with our number computation. It would be necessary of the stronger TNFRSF11B development function to inflammation, brain morphology and cognition of HIVE.

Background: Cancer outcomes studies primarily enroll white patients, and symptom care standards are based overwhelmingly on white patients’ experiences, even though many analyses have shown ethno-racial disparities in symptom burden. This study seeks to better understand ethno-racial disparities in the symptom burden of brain and spine tumor patients using novel computational network analysis (NA) approaches. NA identifies complex symptom co-severity patterns across large patient cohorts. This is the first study to use NA to analyze ethno-racial disparities in cancer symptom burden. Methods: Symptom severity data reported using the MD Anderson Symptom Inventory from a two-institutional cohort of 1651 brain and spine tumor patients was analyzed. Symptom data from white (n = 1,382) and non-white patients (n = 269) were analyzed separately. All non-white patients were analyzed together due to sample size limitations. Gaussian Graphical Model networks were constructed for each group. Network characteristics were analyzed and compared using permutation-based statistical tests. Results: Networks for white and non-white patients were constructed with high and moderate accuracy, respectively. Assessment of strength centrality, a measure of how core a symptom is to overall symptom burden, and betweenness centrality, a measure of how much a symptom contributes to the severity of other symptoms, revealed several key symptoms for each group. For white patients, fatigue, nausea, and distress/feeling upset had the highest strength, whereas fatigue, nausea, and pain had the highest betweenness. For non-white patients, drowsiness, disturbed sleep, and sadness had the highest strength, whereas change in appetite, disturbed sleep, sadness, and pain had the highest betweenness. The two network architectures were statistically different from each other in the Network Comparison Test (p = 0.033). White patients had significantly higher strength for fatigue (p = 0.032) and betweenness for nausea (p = 0.005), whereas non-white patients had significantly higher strength for sleep disturbance (p = 0.005). Independent t-test analysis only identified drowsiness as being significantly different between the two groups (p = 0.032), emphasizing how NA can reveal differences in symptom burden undiscoverable by traditional analyses. Discussion: Our results demonstrate that white and non-white patients experience different symptom co-severity patterns, with symptoms such as sleep disturbance being more influential in non-white patients and symptoms such as fatigue and nausea more significant in white patients. These results underscore the importance of considering the differences (and similarities) in symptomatology of patients from different racial and ethnic backgrounds. This will help address disparities and provide more personalized and effective care for diverse brain and spine tumor patient populations. Citation Format: Brandon H. Bergsneider, Elizabeth Vera, Mark R. Gilbert, Terri S. Armstrong, Orieta Celiku. PIONEER: Computational Probing of dIfferences in symptOms and fuNction of divErsE brain and spine tumoR populations [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C001.

The global discourse on artificial intelligence (AI) ethics represents a critical site of scientific and expert communication, where meanings are negotiated, and priorities are set. This study investigates how a transnational network of experts constructs and communicates the concept of “responsible AI.” We analyze the deliberative discourse from the World University Network (WUN) initiative on Responsible & Ethical AI (2023) through a multi-method framework combining computational text analysis (TF-IDF) and network analysis (co-occurrence networks) of semantic relationships. By examining expert webinar transcripts, we move beyond isolated principles to map the communicative architecture of this debate, visualizing how core themes like accountability, transparency, and equity are framed and interconnected across academic, policy, and practitioner perspectives. Our findings reveal that expert consensus is built not on a glossary of terms but on a shared conceptual network where technical, governance, and ethical concerns are deeply intertwined. This study contributes to science communication research by: (1) offering a novel methodological pipeline for mapping consensus and divergence in expert discourse, and (2) providing empirical evidence that collaborative academic networks function as vital “communicative infrastructures” for translating theoretical ethical frameworks into actionable policy paradigms.

: This report documents the intersection of computational social network analysis and sociolinguistic research aimed at discovering how social intent is communicated through online bilingual speech acts in African cultures. Researchers from the US Army Research Lab (ARL) and Howard University (HU) exchanged information, data, and analyses to examine the feasibility of using automated text analytics software to provide contextual understanding within a text corpus. This effort extends the Army Research Office Partners in Research Transition program titled Extracting Social Meaning from Linguistic Structures Involving Code-Switching in English (and French) with Selected African Languages led by HU. It also provided test and evaluation opportunities for ARL prototype software designed to extract relational networks and sentiment from unstructured Tweets. This collaboration was driven by the realization that more social input is needed to refine context for sociolinguistic analysis and also by the increasing importance of modeling social issues for military decision making. To address social communication acts, we focus on using Twitter for sharing individual and collective opinions. Social media services in general have gained popularity in recent years and are frequently used for discovery and analysis of social intent. We examine the sociolinguistic features that can be used to discover social intent, discuss how social network analysis can be used to inform contextual nuances in which that intent is communicated, and describe how automated tools can be used to support sociolinguistic analysis. We conclude with future research directions that can extend the rich connections between computational social network analysis and the study of sociolinguistics.

This data‐driven study framed in the interactionist approach investigates the influence of social graph topology and peer interaction dynamics among foreign exchange students enrolled in an intensive German language course on second language acquisition (SLA) outcomes. Applying the algorithms and metrics of computational social network analysis (SNA), we find that (a) the best predictor of target language (TL) performance is reciprocal interactions in the language being acquired, (b) the proportion of output in the TL is a stronger predictor than input (Principle of Proportional Output), (c) there is a negative relationship between performance and interactions with same‐first‐language speakers, (d) a significantly underperforming English native‐speaker dominated cluster is present, and (e) there are more intense interactions taking place between students of different proficiency levels. Unlike previous study abroad social network research concentrating on the microlevel of individual learners’ egocentric networks and presenting an emic view only, this study constitutes the first application of computational SNA to a complete learner network (sociogram). It provides new insights into the link between social relations and SLA with an etic perspective, showing how social network configuration and peer learner interaction are stronger predictors of TL performance than individual factors such as attitude or motivation, and offering a rigorous methodology for investigating the phenomenon.

In today’s information technology age, our political discourse is shrinking to fit our smartphone screens. Further, with the availability of inexpensive and ubiquitous mass communication tools like social media, disseminating false information and propaganda is both convenient and effective. Groups use social media to coordinate cyber propaganda campaigns in order to achieve strategic and political goals, influence mass thinking, and steer behaviors or perspectives about an event. In this research, we study the online deviant groups (ODGs) who created a lot of cyber propaganda that were projected against the NATO’s Trident Juncture Exercise 2015 (TRJE 2015) on both Twitter and blogs. Anti-NATO narratives were observed on social media websites that got stronger as the TRJE 2015 event approached. Call for civil disobedience, planned protests, and direct action against TRJE 2015 propagated on social media websites. We employ computational social network analysis and cyber forensics informed methodologies to study information competitors who seek to take the initiative and the strategic message away from NATO in order to further their own agenda. Through social cyber forensics tools, e.g., Maltego, we extract metadata associated with propaganda-riddled websites. The extracted metadata helps in the collection of social network information (i.e., friends and followers) and communication network information (i.e., network depicting the flow of information such as tweets, retweets, mentions, and hyperlinks). Through computational social network analysis, we identify influential users and powerful groups (or the focal structures) coordinating the cyber propaganda campaigns. The study examines 21 blogs having over 18,000 blog posts dating back to 1997 and over 9000 Twitter users for the period between August 3, 2014, and September 12, 2015. These blogs were identified, crawled, and stored in our database that is accessible through the Blogtrackers tool. Blogtrackers tool further helped us identify the activity patterns of blogs, keyword patterns, and the influence a blog or a blogger has on the community, and analyze the sentiment diffusion in the community.

The identification and engagement of stakeholders is a challenge whose outcomes have a strong impact on a project’s success. This is even more relevant when the project concerns the introduction of sustainable technologies; these technologies are often less competitive on the market than traditional ones, both in terms of development complexity and production costs. This paper presents a stakeholder identification and mapping procedure, based on an Interest x Influence model, that emphasizes a quantitative methodological approach. The method has been applied on publicly available online data to identify and map potential stakeholders of a European research project aiming at creating a new biomass-derived biofuel. A semi-supervised procedure, built by combining computational text analysis and social network analysis techniques, has been used to calculate Interest and Influence scores for each potential stakeholder toward the project. The results show that stakeholders can be ranked on both dimensions and mapped on a bi-dimensional space according to their level of Interest and Influence. Within projects aiming at developing technologies for sustainability in which a wide range of stakeholders are involved at a transnational level, this stakeholder mapping technique provides a useful tool that can be adopted even with little knowledge on specific fields of application. A further asset of this approach lies in the possibility of profiling stakeholders on the basis of their Interest in the target project: this allows us to know the contents of a stakeholder (or stakeholders category) Interest, and therefore to have useful information for addressing the targeted stakeholder by means of a content design which is based on specific content categories, substantiating the stakeholder(s) Interest in the specific project.

Using computational Social Network Analysis (SNA), this longitudinal study investigates the development of the interaction network and its influence on the second language (L2) gains of a complete cohort of 41 U.S. sojourners enrolled in a 3‐month intensive study‐abroad Arabic program in Jordan. Unlike extant research, our study focuses on students’ interactions with alma mater classmates, reconstructing their complete network, tracing the impact of individual students’ positions in the social graph using centrality metrics, and incorporating a developmental perspective with three measurement points. Objective proficiency gains were influenced by predeparture proficiency (negatively), multilingualism, perceived integration of the peer learner group (negatively), and the number of fellow learners speaking to the student. Analyses reveal relatively stable same‐gender cliques, but with changes in the patterns and strength of interaction. We also discuss interesting divergent trajectories of centrality metrics, L2 use, and progress; predictors of self‐perceived progress across skills; and the interplay of context and gender.

Abstract. Risdiyanto I, Santosa Y, Santoso N, Sunkar A. 2024. Determining key mammalian species and food web robustness across different land cover vegetation using network analysis. Biodiversitas 25: 3175-3190. The ecosystem's food web depicts the intricate energy flow and complex interactions among its diverse organisms. Organisms are not confined to single trophic levels or food chains; they establish multiple food connections with other organisms within the ecosystem. This study aims to identify critical mammalian species and assess the robustness of various ecosystem types, considering the diversity of species presence, through food web analysis. Computational graphic-based network analysis is employed to achieve this goal. Ecosystem types, categorized by their land cover, include plantation/agricultural forests, Bushes and shrubss, forests, and mixed landscapes. Network centrality metrics such as degree, closeness, betweenness, and eigenvector centrality are utilized to evaluate the presence of key species and ecosystem robustness. The relative contribution of mammalian species as connectors and regulators of energy flow in the food web ranges from 8-23% of all nodes involved. Key mammalian species are classified into ecosystem stability and sustainability keys. In Bushes and shrubss ecosystems, key species predominantly consist of mammalian predator species that are crucial for maintaining ecosystem stability through population control. Conversely, in other ecosystems, key species are primarily connectors, ensuring the sustained energy flow. The most resilient food webs are observed in Bushes and shrubss and mixed ecosystems due to their higher biomass growth rates and abundant presence of mammalian species. Utilizing food web analysis can significantly contribute to species and ecosystem conservation efforts by offering a comprehensive understanding of interspecies interactions, food web structures, and ecosystem dynamics.

Melanoma, a highly aggressive malignancy characterized by rapid metastasis and elevated mortality rates, predominantly originates in cutaneous tissues. While surgical interventions, immunotherapy, and targeted therapies have advanced, the prognosis for advanced-stage melanoma remains dismal. Globally, melanoma incidence continues to rise, with the United States alone reporting over 100,000 new cases and 7,000 deaths annually. Despite the exponential growth of tumor data facilitated by next-generation sequencing (NGS), current analytical approaches predominantly emphasize single-gene analyses, neglecting critical insights into complex gene interaction networks. This study aims to address this gap by systematically exploring immune gene regulatory dynamics in melanoma progression. We developed a bidirectional, weighted, signed, and directed topological immune gene regulatory network to compare transcriptional landscapes between benign melanocytic nevi and cutaneous melanoma. Advanced network analysis tools were employed to identify structural disparities and functional module shifts. Key driver genes were validated through topological centrality metrics. Additionally, deep learning models were implemented to predict drug-target interactions, leveraging molecular features derived from network analyses. Significant topological divergences emerged between nevi and melanoma networks, with dominant functional modules transitioning from cell cycle regulation in benign lesions to DNA repair and cell migration pathways in malignant tumors. A group of genes, including AURKA, CCNE1, APEX2, and EXOC8, were identified as potential orchestrators of immune microenvironment remodeling during malignant transformation. The deep learning framework successfully predicted 23 clinically actionable drug candidates targeting these molecular drivers. The observed module shift from cell cycle to invasion-related pathways provides mechanistic insights into melanoma progression, suggesting early therapeutic targeting of DNA repair machinery might mitigate metastatic potential. The identified hub genes, particularly AURKA and DDX19B, represent novel candidates for immunomodulatory interventions. Our computational drug prediction strategy bridges molecular network analysis with clinical translation, offering a paradigm for precision oncology in melanoma. Future studies should validate these targets in preclinical models and explore network-based biomarkers for early detection.

The white water lily (Nymphaea alba) is a traditional medicinal plant recognized for its diverse array of bioactive properties. However, its potential in wound healing remains largely unexplored. This study aimed to evaluate the phytochemical profile, cytotoxicity, and wound healing efficacy of Nymphaea alba flower extract (NAFE) using both in vitro and in vivo models, as well as computational network analysis. Qualitative phytochemical screening of NAFE was conducted using standard techniques. Cytotoxicity was assessed on HaCaT keratinocyte cells at concentrations ranging from 0 to 1000 μg/ml. In vivo wound healing was evaluated using excision wound models in Wistar albino rats treated with 2.5% and 5% NAFE ointments, measuring wound contraction, epithelialization time, and breaking strength. In vitro scratch assays were used to assess cell migration at selected concentrations of NAFE. A wound-healing-associated network analysis was performed using IMPPAT, STRING, GeneCards, and OMIM databases to explore the molecular targets and interactions of bioactive compounds. Phytochemical analysis confirmed the presence of alkaloids, flavonoids, phenolics, tannins, and glycosides. NAFE was found to be non-cytotoxic with an IC50 of 245 μg/ml. In vivo, 5% NAFE ointment showed 98.92% wound closure by day 14 and complete closure by day 21, comparable to betadine. Epithelialization time (15.83±0.16 days) was nearly equivalent to the standard drug. In vitro assays demonstrated enhanced HaCaT cell migration at concentrations of 122.5 and 245 μg/ml. Network analysis identified kaempferol and quercetin as key compounds interacting with wound-healing proteins, notably AKT1, ESR1, and EGFR. The findings suggest that NAFE promotes wound healing by enhancing wound contraction, epithelialization, and cell migration, likely through the modulation of molecular pathways involved in tissue repair. The presence of bioactive compounds such as kaempferol and quercetin underpins the extract's pharmacological potential. Nymphaea alba flower extract exhibits promising wound-healing activity through multiple mechanisms, including enhancement of cell migration and regulation of key proteins involved in tissue regeneration. These results support its potential as a natural therapeutic agent in wound management.