Cross-platform Analysis Research Articles

Public discussion of family location tracking apps: a cross-platform analysis of social media posts about Life360

ABSTRACT This paper contributes to research on family location tracking technologies by investigating public discussion about the uses, meanings, and impacts of location tracking apps. The study offers a topic-based qualitative content analysis of public posts about the most popular location tracking app Life360 across three key social media platforms—X (formerly Twitter), YouTube and TikTok. It adopts a cross-platform perspective to gather and analyse varied viewpoints from these platforms, which differ in their social media content, user demographics, and technical affordances. The analysis reveals an extensive range of public discussion topics, which are categorised into three themes: Family Experiences, Views and Values; News, Commerce and Information; and Critique, Subversion, and Humour. The themes align with the different platform vernaculars of each platform, with X posts spread across family experience, news and criticial commentary, whilst YouTube emphasises more commercial and informational topics, and TikTok surfaces more humour, subversive or critical posts, especially from younger users. Furthermore, the cross-platform analysis highlights how the personal, political, and subversive posts from teenage users on TikTok influences the wider news and public discussions about family location tracking and surveillance across other social and legacy media.

Network alignment in multiplex social networks using the information diffusion dynamics

Modern social networking applications (SNAs) typically rely on independent data management systems, leading to fragmented user identities and the creation of data silos. This fragmentation impedes the development of unified user profiles and limits the effectiveness of cross-platform behavioural analysis and personalized recommendations. As the demand for integrated services grows, the need for a unified user identity across platforms becomes increasingly critical. However, existing identity integration methods face significant challenges, including data isolation, privacy risks, and a lack of universal standards. To address these issues, we propose a framework that utilizes node dynamics time-series data for network alignment in multiplex social networks. Our approach employs the UIU diffusion model to simulate information diffusion dynamics across multiple platforms and uses the Expectation-Maximization (EM) algorithm for network alignment. Crucially, our method relies solely on publicly available user information from different platforms, avoiding the need for access to private user data, thereby enhancing security. Experimental results demonstrate the model’s efficacy, achieving F1 scores of 100% for interlayer links and over 90% for intralayer links on real-world datasets. These findings highlight the model’s potential for applications in social influence analysis, community detection, and recommendation systems.

A spatial human thymus cell atlas mapped to a continuous tissue axis

T cells develop from circulating precursor cells, which enter the thymus and migrate through specialized subcompartments that support their maturation and selection1. In humans, this process starts in early fetal development and is highly active until thymic involution in adolescence. To map the microanatomical underpinnings of this process in pre- and early postnatal stages, we established a quantitative morphological framework for the thymus—the Cortico-Medullary Axis—and used it to perform a spatially resolved analysis. Here, by applying this framework to a curated multimodal single-cell atlas, spatial transcriptomics and high-resolution multiplex imaging data, we demonstrate establishment of the lobular cytokine network, canonical thymocyte trajectories and thymic epithelial cell distributions by the beginning of the the second trimester of fetal development. We pinpoint tissue niches of thymic epithelial cell progenitors and distinct subtypes associated with Hassall’s corpuscles and identify divergence in the timing of medullary entry between CD4 and CD8 T cell lineages. These findings provide a basis for a detailed understanding of T lymphocyte development and are complemented with a holistic toolkit for cross-platform imaging data analysis, annotation and OrganAxis construction (TissueTag), which can be applied to any tissue.

Platform affordances, discursive opportunities, and social media activism: A cross-platform analysis of #MeToo on Twitter, Facebook, and Reddit, 2017–2020

This study proposes affordances for discursive opportunities (ADO) as a theoretical framework that leverages the concept of technological affordances and the theory of discursive opportunities to understand platform potential in shaping social media activism. Specifically, ADO underscores how social media platform affordances (e.g., algorithmic curation, shared group identity and culture, connectivity) shape movement visibility, resonance, and legitimacy, all of which can mobilize activism efforts. We further situate our discussion in the cross-platform context to explore both differences and interconnections between different platforms in facilitating digital activism. By analyzing 20,363,128 English posts related to the #MeToo movement from 2017 to 2020 on Twitter, Facebook, and Reddit using supervised machine learning and time series analysis, we reveal platform variations in affording discursive opportunities for visibility, resonance, and legitimacy, which shape activism efforts differently across platforms. We also find a unidirectional relationship from activism on Twitter and Reddit to activism on Facebook.

UeLP: accurate user linkage across social platforms against location errors

Abstract User linkage across social platforms can connect the accounts of the same user across different social networks, which is crucial for the identification of users’ multiple social identities and cross-platform association analysis. Cross-platform user linkage based on location is a typical method in current research. These methods typically rely on check-in data to calculate user similarity. However, different from check-in location, the location data obtained from instant messaging social platforms may contain random errors, leading to low accuracy of user linkage of such methods. To solve this problem, this paper proposes an accurate user linkage method across social platforms against location errors. First, unlike existing methods that employ fixed-size grids, this paper uses a multi-grained spatio-temporal grid to organize data, in order to accurately extract user features from error locations. Then, by extracting coarse-grained movement pattern features from user trajectories, candidate users are filtered out, and a small subset of candidate uses is generated to effectively reduce the search space. Next, we establish a weight model based on grid contribution and motion sequence similarity to extract location and temporal features with stronger user orientation. Finally, according to the weight model, the weighted cluster center distance of trajectories is used to calculate the similarity between two different user trajectories. The user with the highest similarity is selected from the candidate subset to complete the user linkage. The extensive experiments are conducted on six public datasets containing 115 866 trajectories and a self-built dataset with 5358 trajectories. The results show the following: compared with the four existing typical location-based methods $k$-BCT, GS, TF-IDF, and TF-IWF, the accuracy Acc@1 is improved by an average of 33%, 44.94%, 15.2%, and 14.55%, respectively, and the accuracy Acc@3 is improved by 30.52%, 34.67%, 13.84%, and 13.19%, respectively.

From CSV to Arrow: Creating a Unified Data Set for Efficient Cross-Platform Analysis

Handling open data, like the vast repository of New York City (NYC) 311 service requests, often starts with the ubiquitous CSV (comma-separated value) file format. However, CSV files are notoriously inefficient for curation, bogged down by redundancy and potential misinterpretations. Enter Apache Arrow, a game-changing approach that not only slashes storage requirements but also primes data for seamless analysis across popular platforms like R, Python, and Julia. Using the NYC 311 service request data, we demonstrate the conversion of a CSV file to the Arrow IPC (Inter-Process Communication) format. An Arrow file stores the table schema with the data in a binary format that can be memorymapped for reading, enabling instantaneous access to potentially large datasets. The Arrow IPC data serves as a universal starting point for analysis across various environments. In our example, this conversion is done in Julia, which has powerful packages for reading and writing CSV or Arrow files and calling functions in other popular environments such as R and Python.

A comparative cross-platform analysis of cuproptosis-related genes in human nonobstructive azoospermia: An observational study.

This study aimed to identify novel biomarkers associated with cuproptosis in human nonobstructive azoospermia (NOA). We obtained 4 NOA microarray datasets (GSE145467, GSE9210, GSE108886, and GSE45885) from the NCBI Gene Expression Omnibus database and merged them into training set. Another NOA dataset (GSE45887) was used as validation set. Differentially expressed cuproptosis-related genes were identified from training set. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination were used to identify hub cuproptosis-related genes. We calculated the expression of the hub cuproptosis-related genes in both validation set and patients with NOA. Gene set variation analysis was used to explore their potential biological functions. The risk prediction model was built by logistic regression analysis and was evaluated in the validation set. Finally, we constructed a competing endogenous RNA network. The training set included 29 patents in the control group and 92 in the NOA group, and 10 cuproptosis-related differentially expressed genes were identified. Subsequently, we screened 6 hub cuproptosis-related genes (DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1) by least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination. GCSH, NFE2L2, NLRP3, and SLC31A1 expressed higher in NOA group than in control group (P < .05) in the validation set (4 patients in control and 16 in NOA groups), while the expression levels of GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 were higher in NOA group than in control group (P < .05) in our patients (3 patients in control and 4 in NOA groups). The model based on the 6-gene signature showed superior performance with an AUC value of 0.970 in training set, while 1.0 in validation set. Gene set variation analysis revealed a higher enrichment score of "homologous recombination" in the high expression groups of the 6 hub genes. Finally, we constructed a competing endogenous RNA network and found hsa-miR-335-3p and hsa-miR-1-3p were the most frequently related to the 6 hub genes. DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 may serve as predictors of cuproptosis and play important roles in the NOA pathogenesis.

Research on the evolution of cross-platform online public opinion for public health emergencies considering stakeholders.

To explore the different processes of the themes and emotional evolution of various stakeholders in the network public opinion of sudden public health emergencies at different stages of the public opinion evolution lifecycle. This paper proposes a cross-platform analysis method for online public opinion during the public health emergencies based on stakeholders. Firstly, data from multiple platforms are collected and integrated. Secondly, stakeholders are categorized and the stages of public opinion evolution are determined based on stakeholder theory and lifecycle theory. Finally, the Latent Dirichlet Allocation (LDA)+Word2vec model and Convolutional Neural Network (CNN) model are used to analyze the themes and emotional evolution of stakeholders during different stages of public opinion evolution. There are differences in the evolution patterns of different types of stakeholders. The evolution process of stakeholders' focus points exhibits a two-stage transition from concentration to divergence. The focus points of stakeholders are closely associated with their respective social domains. The emotions of the public undergo a three-stage process of positive-negative-positive change. This study can provide a reference for the government to have a more comprehensive understanding of the development trend of public opinion and reduce the negative impact of public opinion.

Special issue on methods in visual politics and protest

This special issue forms the second part of a double issue on methods in visual politics and protest. It draws together five articles that provide new pathways for deconstructing visual political narratives and offers reflexive and nuanced accounts for researching visual data and information shared on social media platforms (here: TikTok, Instagram, Twitter/X, Facebook). They do so through the application of feminist mixed methods (femmix), cross-platform analysis, and context-aware, comparative, and triangulated approaches. Taken together, the double issue offers a substantive compendium of articles exploring the latest methodological developments in visual politics and protest.

Social big data mining for the sustainable mobility and transport transition: findings from a large-scale cross-platform analysis

The paper reports findings from a study that examining how cross-platform social media analysis can help to map the digital discourse on sustainable mobility and sustainable transport, and enhance the understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013–2021) and 8,089 Instagram images including captions (2017/2018–2021) were scraped using the Python modules Twint and Instaloader. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an automated machine learning-based image analysis of the Instagram images was conducted using object detection via OpenCV. Synthesized results formed the base for a cross-platform analysis inspired by Rogers’ method comprising hot topics/key themes, user mentions, sentiment polarity, and co-hashtags. Notably, electromobility emerged as a prominent theme, particularly on Instagram, while #sustainabletransport was closely associated with active travel, notably bicycling, and #sustainablemobility showcased a dominance of electromobility discourse. The study demonstrates the investigative potentials of cross-platform social media analysis studies to enhance the understanding of sociotechnical low-carbon transport transitions. Drawing on key results, the paper suggests an adapted version of the Geelsean Multi-Level Perspective on Sociotechnical Transitions.

Automated, High-Throughput Platform to Generate a High-Reliability, Comprehensive Rectal Cancer Database.

Dynamic operations platforms allow for cross-platform data extraction, integration, and analysis, although application of these platforms to large-scale oncology enterprises has not been described. This study presents a pipeline for automated, high-fidelity extraction, integration, and validation of cross-platform oncology data in patients undergoing treatment for rectal cancer at a single, high-volume institution. A dynamic operations platform was used to identify patients with rectal cancer treated at MD Anderson Cancer Center between 2016 and 2022 who had magnetic resonance imaging (MRI) imaging and preoperative treatment details available in the electronic health record (EHR). Demographic, clinicopathologic, tumor mutation, radiographic, and treatment data were extracted from the EHR using a methodology adaptable to any disease site. Data accuracy was assessed by manual review. Accuracy before and after implementation of synoptic reporting was determined for MRI data. A total of 516 patients with localized rectal cancer were included. In the era after institutional adoption of synoptic reports, the dynamic operations platform extracted T (tumor) category data from the EHR with 95% accuracy compared with 87% before the use of synoptic reports, and N (lymph node) category with 88% compared with 58%. Correct extraction of pelvic sidewall adenopathy was 94% compared with 78%, and extramural vascular invasion accuracy was 99% compared with 89%. Neoadjuvant chemotherapy and radiation data were 99% accurate for patients who had synoptic data sources. Using dynamic operations platforms enables automated cross-platform integration of multiparameter oncology data with high fidelity in patients undergoing multimodality treatment for rectal cancer. These pipelines can be adapted to other solid tumors and, together with standardized reporting, can increase efficiency in clinical research and the translation of actionable findings toward optimizing patient outcomes.

Exploring equity perception of electric vehicles from a social media perspective

Electric vehicles (EVs) are considered a promising solution for climate change. However, their introduction has raised uncertainties in transportation equity, reflecting the diverse public perceptions and discussions on this issue. Understanding these perceptions is crucial for integrating equity into EV adoption effectively. To bridge this gap, our study used a social media approach to conduct a cross-platform equity analysis on two popular online social networks (OSNs): Twitter and Reddit. We identified 22,533 tweets and 12,318 Reddit comments relevant to equity from all EV-related discussions from 2011 to 2022. Our analysis utilized different features of the two OSNs, including verified account identity and Reddit communities, to present a comprehensive picture of equity perception in social media. Additionally, we used machine learning methods to infer Twitter users’ demographic attributes and integrate user demographics into equity analysis. Our findings show that political accounts are the center of attention on Twitter, but they are accompanied by negative attitudes. Many fringe communities are highly active in equity discussions on Reddit. Our study also found that younger people pay less attention and hold more negative sentiments towards equity discussions, while male and female users have similar interests in equity and similar sentiment. Notably, the proportion of equity discussions remains low in the overall EV corpus, despite a significant increase in recent years.

Brazilian Capitol attack: The interaction between Bolsonaro’s supporters’ content, WhatsApp, Twitter, and news media

Bolsonaro’s supporters used social media to spread content during key events related to the Brasília attack. An unprecedented analysis of more than 15,000 public WhatsApp groups showed that these political actors tried to manufacture consensus in preparation for and after the attack. A cross-platform time series analysis showed that the spread of content on Twitter predicted the spread of content on WhatsApp. Twitter also predicted news coverage of Bolsonaro’s supporters, suggesting a propaganda feedback loop. Our findings indicate that investigative journalism and public policy initiatives could benefit from monitoring public groups on encrypted messaging apps.

Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery.

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.

The Russia–Ukraine War in Chinese Social Media: LLM Analysis Yields a Bias Toward Neutrality

This study is a cross-platform analysis of the discourses surrounding the Russia–Ukraine war in Chinese social media. Making use of both manual as well as automated classification of discussion about the war, we found most significantly the mass amplification of Russian state positions on Weibo and the reframing of the war as being in the Chinese national self-interest on Douyin. We situate what we call cross-national amplification as well as the national self among other notions that seek to capture the broad discursive power of the Chinese state including digital nationalism, soft propaganda, and playful patriotism. A second set of findings include some agreement between the manual and automated classification, albeit with the artificial intelligence (AI)-assisted platforms showing what we call a bias toward neutrality. We also emphasize the importance of a cross-platform analysis (rather than a single-platform analysis) when seeking to capture public sentiment on social media and the types of orchestrated, state discursive power on display.

1242 Metabologenomic Characterization Uncovers Heterogeneity Amongst IDH Mutant Gliomas

INTRODUCTION: Mutations in isocitrate dehydrogenase (IDH) enzymes are recognised to drive the molecular footprint of diffuse gliomas through the accumulation of oncometabolite R-2-hydroxyglutarate which drives the widespread restructuring of the DNA methylome; however, beyond R-2-hydroxyglutarate, a comprehensive metabologenomic characterization of IDH-mutant gliomas has yet to be performed. METHODS: Glioma samples from a cohort of 154 patients underwent multiplatform molecular analysis, including metabolomic studies, genome-wide DNA methylation profiling and bulk RNA sequencing. A integrative analysis was performed, including hierarchical clustering and principal component analysis of identified metabolites, differentially methylated probes, copy number alteration and bulk mRNA data. Survival analysis as well as multivariable hazard ratios for clinical variables were calculated by fitting Cox Proportional Hazards Models. RESULTS: We discovered a group of IDH-mutant gliomas whose metabolic profile highly resembled IDH wildtype tumors. Notably, these IDH-mutant gliomas with dysregulated metabolism were distinguished from their IDH-mutant counterparts by significantly shorter overall survival (median OS 118.9 months vs 173.6 months, p=0.048). The IDH-mutant tumors with dysregulated metabolism harbored distinct epigenetic alterations that converged to drive proliferative and stem-like transcriptional profiles. The prognostic relevance of dysregulated metabolism complements, but was not wholly explained by canonically recognized prognostic classifications in IDH-mutant gliomas including 1p/19q codeletion, glioma CpG Island Hypermethylator (GCIMP) status and CDKN2A homozygous deletion. CONCLUSIONS: Utilizing a cross-platform analysis we have uncovered a novel subtyping of IDH-mutant gliomas with dysregulated cellular metabolism with similar survival to IDH-wildtype tumors. The metabolic profile provides unique information on glioma phenotypes, which may facilitate a more comprehensive understanding of glioma biology, and provide a window to target novel dependencies.

FAIR Header Reference genome: a TRUSTworthy standard.

The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability and Technology. The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.

Convergence or divergence? A cross-platform analysis of climate change visual content categories, features, and social media engagement on Twitter and Instagram

Advocacy organizations increasingly leverage social media and visuals to communicate complex climate issues. By examining an extensive dataset of visual posts collected from five organization accounts on two multimodal social media platforms, Twitter and Instagram, we conducted a cross-platform comparison of visual content categories and visual features related to climate change. Through deep-learning-based unsupervised image clustering, we discovered that visual content on both platforms could be broadly classified into five categories: infographics/captioned images, nature landscape/wildlife, climate activism, technology, and data visualization. However, these categories were not equally represented on each platform. Instagram featured more nature landscape/wildlife content, while Twitter showed more infographics/captioned images and data visualization. Through computational visual analysis, we found that the two platforms also presented significant differences in overall warm and cool colors, brightness, colorfulness, visual complexity, and presence of faces. Additionally, we identified platform-specific patterns of engagement associated with these categories and features. With the urgency to address climate change, these findings can guide climate advocacy organizations in developing strategies tailored to each platform’s specific characteristics for maximum effectiveness. This study highlights the significance of using computational methods in efficiently uncovering meaningful themes from extensive visual data and quantifying aesthetic features in strategic communication.

Multidimensional sentiment analysis method on social media data: comparison of emotions during and after the COVID-19 pandemic

PurposeThis study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.Design/methodology/approachA mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.FindingsThe results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.Research limitations/implicationsThe primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.Practical implicationsUnderstanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.Social implicationsThe study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.Originality/valueThis research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

TMET-06. METABOLOGENOMIC CHARACTERIZATION UNCOVERS HETEROGENEITY AMONGST IDH MUTANT

Abstract INTRODUCTION Mutations in isocitrate dehydrogenase (IDH) enzymes are recognised to drive the molecular footprint of diffuse gliomas through the accumulation of oncometabolite R-2-hydroxyglutarate which drives the widespread restructuring of the DNA methylome; however, beyond R-2-hydroxyglutarate , a comprehensive metabologenomic characterization of IDH-mutant gliomas has yet to be performed. We aimed to characterize the global metabolic profile of IDH-mut and IDH-wt gliomas, identify clinically relevant subgroups based on distinct metabolic differences, and utilize matched epigenetic and transcriptional data to characterize these distinct groups. Method: Glioma samples from a cohort of 154 patients underwent multiplatform molecular analysis, including metabolomic studies, genome-wide DNA methylation profiling and bulk RNA sequencing. A integrative analysis was performed, including hierarchical clustering and principal component analysis of identified metabolites, differentially methylated probes, copy number alteration and bulk mRNA data. Survival analysis as well as multivariable hazard ratios for clinical variables were calculated by fitting Cox Proportional Hazards Models. RESULTS We discovered a group of IDH-mutant gliomas whose metabolic profile highly resembled IDH wildtype tumors. Notably, these IDH-mutant gliomas with dysregulated metabolism were distinguished from their IDH-mutant counterparts by significantly shorter overall survival (median OS 118.9 months vs 173.6 months, p=0.048). The IDH-mutant tumors with dysregulated metabolism harbored distinct epigenetic alterations that converged to drive proliferative and stem-like transcriptional profiles. The prognostic relevance of dysregulated metabolism complements, but was not wholly explained by canonically recognized prognostic classifications in IDH-mutant gliomas including 1p/19q codeletion, glioma CpG Island Hypermethylator (GCIMP) status and CDKN2A homozygous deletion. CONCLUSION Utilizing a cross-platform analysis we have uncovered a novel subtyping of IDH-mutant gliomas with dysregulated cellular metabolism with similar survival to IDH-wildtype tumors. The metabolic profile provides unique information on glioma phenotypes, which may facilitate a more comprehensive understanding of glioma biology, and provide a window to target novel dependencies.