Social Media Data Research Articles

Social Media Data Privacy Related to Security Awareness and Student Trust Regarding Data on Instagram

Instagram, as one of the most popular social media sites, has brought many new trends. Many people use Instagram to express themselves or share content through photos and videos. While social media data privacy is important, many people still share their daily activities and personal data. This causes a lot of personal information disclosure, which can lead to the potential for crime or misuse of the scattered data, considering that data is crucial today. Therefore, this research was conducted to determine people's social media data privacy and security awareness on Instagram, especially those with a data-related educational background. This research uses quantitative descriptive methods, distributing questionnaires through Google Forms in group chats, personal chats, or questionnaires to respondents directly. The population in this study is made up of students with a computing program background. It also used purposive sampling to determine the number of 153 samples. From the descriptive analysis, it is known that most respondents are aware of the vulnerability of social media data privacy on Instagram. This can be seen from respondents who know what data is used by Instagram, where they also monitor login activity. Respondents also secure their Instagram accounts by not using the same password as other social media accounts. However, in certain cases, some respondents still need to realize this awareness, so education is still needed regarding the importance of social media data privacy, especially on Instagram. Doi: 10.28991/HIJ-2024-05-02-015 Full Text: PDF

KETCH: A Knowledge-Enhanced Transformer-Based Approach to Suicidal Ideation Detection from Social Media Content

Suicide is a major cause of death among 15- to 29-year-olds globally, claiming more than 50,000 lives in the United States in 2023 alone. Despite governmental efforts to provide support, many individuals experiencing suicidal thoughts do not seek help but are increasingly turning to social media to express their feelings. This trend offers a critical opportunity for timely detection and intervention of suicidal ideation. We develop an innovative transformer-based model for suicidal ideation detection (SID) that combines domain knowledge with dynamic embedding and lexicon-based enhancements. Our model, which is tested on social media data in two languages from different platforms, outperforms existing state-of-the-art models for SID. We have also explored its applicability to detecting depression and its practical implementation in real-world scenarios. Our research contributes significantly to the field, offering new methods for timely and proactive intervention in suicidal ideation, with potential wide-reaching effects on public health, economics, and society. Methodologically, our approach advances the integration of human expertise into AI models to enhance their effectiveness.

Deep learning based Election Results Prediction using Twitter Data

Abstract: This project presents an intelligent system political sentiment analysis with information from Twitter. . The system utilizes deep learning techniques, specifically a BiLSTM model featuring an attention mechanism, to predict the political affiliation (BJP or Congress) of tweets. The data preprocessing involves cleaning text, data augmentation, tokenization, and indexing. A dataset is used to train the model. consisting of tweets from both parties, getting very accurate results in predicting the sentiment of tweets. Additionally, the software offers functionality for data visualization, including displaying sample tweets, sentiment analysis results, and the winning party. Additionally, an admin page is available for further customization and management. Overall, this project the possibilities of Using deep learning to analyze sentiment of political content on social media data.

Using social media data to identify neighbourhood change

Using social media data to identify neighbourhood change

Integrating Traditional and Social Media Data to Predict Bilateral Migrant Stocks in the European Union

Although up-to-date information on the nature and extent of migration within the European Union (EU) is important for policymaking, timely and reliable statistics on the number of EU citizens residing in or moving across other member states are difficult to obtain. In this paper, we develop a statistical model that integrates data on EU migrant stocks using traditional sources such as census, population registers and Labour Force Survey, with novel data sources, primarily from the Facebook Advertising Platform. Findings suggest that combining different data sources provides near real-time estimates that can serve as early warnings about shifts in EU mobility patterns. Estimated migrant stocks match relatively well to the observed data, despite some overestimation of smaller migrant populations and underestimation for larger migrant populations in Germany and the United Kingdom. In addition, the model estimates missing stocks for migrant corridors and years where no data are available, offering timely now-casted estimates.

A novel and secured email classification using deep neural network with bidirectional long short-term memory

Email data has some characteristics that are different from other social media data, such as a large range of answers, formal language, notable length variations, high degrees of anomalies, and indirect relationships. The main goal in this research is to develop a robust and computationally efficient classifier that can distinguish between spam and regular email content. The benchmark Enron dataset, which is accessible to the public, was used for the tests. The six distinct Enron data sets we acquired were combined to generate the final seven Enron data sets. The dataset undergoes early preprocessing to remove superfluous sentences. The proposed model Bidirectional Long Short-Term Memory (BiLSTM) apply spam labels and to examine email documents for spam. On seven Enron datasets, DNN-BiLSTM performs better than other classifiers in the performance comparison in terms of accuracy. DNN-BiLSTM and convolutional neural networks demonstrated that they can classify spam with 96.39 % and 98.69 % accuracy, respectively, in comparison to other machine learning classifiers. The risks associated with cloud data management and potential security flaws are also covered in the paper. This research presents hybrid encryption as a means of protecting cloud data while preserving privacy by using the hybrid AES-Rabit encryption algorithm which is based on symmetric session key exchange.

Using social media image to identify threatened elasmobranch species caught by a small-scale fishery in a data-poor area.

Sharing fishing activity via social media attracts millions of views. The data from these shares includes key information for a fisheries database such as the species present in the catches and in many cases the number of specimens caught. Many countries in the Global South do not have official information about basic data on fishing activity, especially small-scale activity. In many of such countries like Brazil, overfishing poses a significant threat to numerous threatened species, including elasmobranchs, due to the frailty of fisheries management and law enforcement. The lack of a national (Brazil) data collection program makes sharing data on social media a potential source of fisheries related information. Hence, this study aimed to verify the potential of social media data to describe artisanal fishing and identify elasmobranch species caught by a small-scale fleet in a data-poor Equatorial Southwest Atlantic region. The identification of elasmobranch species was made based on 250 videos shared by local fishers publicly on social media (Youtube™). Among these, sharks and rays were observed in 19 (8%) and 42 (17%) videos, respectively. A total of 98 specimens were recorded, of which 80% could be identified to a species level. Most of the records comprised the stingrays Hypanus berthalutzae and Hypanus guttatus, while Ginglymostoma cirratum was the most recorded shark. More than half of the elasmobranch species recorded are listed at some level of threat according to the IUCN Red List. According to the Brazilian legislation, at least 20% of the species identified in these videos are considered Endangered (EN) or Critically Endangered (CR). Therefore, fishing activity shared via social media can be considered effective in identifying threatened elasmobranch species, as well as providing data on species occurrence, number of specimens, or rare sightings. It is suggested that fishing activity shared via social media be used in parallel with traditional methods (e.g. on-board data and landing observations) to obtain fisheries information and data about endangered species in data poor areas.

Exploring food poverty experiences in the German Twitter-Sphere

BackgroundThis study investigates the subjective perceptions of food poverty in Germany by analysing Twitter discourse using the German-language hashtag #IchBinArmutsbetroffen (#IamPovertyAffected) and examines the extent to which various dimensions of a multidimensional theoretical model of food poverty are represented in the discourse.MethodsEmploying a combination of computational social science and qualitative social research methods, the research identifies, and analyses tweets related to nutrition by applying a hierarchical dictionary search and qualitative content analysis. By examining the narratives and statements of individuals affected by food poverty, the study also investigates the interplay among different subdimensions of this phenomenon.ResultsThe analysis of 1,112 tweets revealed that 57.96% focused on the material dimension and 42.04% on the social dimension of food poverty, suggesting a relatively balanced emphasis on material and social aspects of food poverty in the narratives of those affected. The findings reveal that tweets on material food poverty underscore economic challenges and resource scarcity for food. Social food poverty tweets demonstrate widespread deprivation in social participation, leading to isolation, exclusion, and social network loss. Overall, the results elucidate intricate interconnections among subdimensions and multidimensional manifestations of food poverty.ConclusionsThis study contributes methodologically by presenting an approach for extracting food-related textual social media data and empirically by providing novel insights into the perceptions and multifaceted manifestations of food poverty in Germany. The results can aid in a better understanding of the phenomenon of food poverty as it currently manifests in Germany, and in developing targeted social, health-promoting, and political measures that address more effectively the empirically evident multidimensionality of the phenomenon.

Research on the Public’s Intention to Use Shared Autonomous Vehicles: Based on Social Media Data Mining and Questionnaire Survey

While the emergence of shared autonomous vehicles can be an effective solution to improve transport issues and achieve sustainable development, the benefits associated with shared autonomous vehicles can only be realized when the public intends to use them. Therefore, it is necessary to conduct an in-depth study on the public’s intention to use shared autonomous vehicles and identify the key influencing factors. This study mined social media data to obtain real public perceptions. A qualitative exploratory analysis was used to identify thematic variables regarding social media data on shared autonomous vehicles, from which a research model of the public’s intention to use SAVs was proposed. Then, a questionnaire survey was conducted, and the structural equation model and Bayesian network were used to analyze the questionnaire data quantitatively. The findings reveal how perceived risk, social information, trust, perceived usefulness, and personality traits affect the public’s intention to use shared autonomous vehicles, and how to enhance the public’s intention to use them. This study will enrich the research on traveler psychology in the context of intelligent travel and provide theoretical basis and decision support for future policies to promote shared autonomous vehicles.

Awareness of Preventive Measures in Osteoarthritis Among Urban Population in Pune City

Background: Osteoarthritis (OA) is the most common articular disease of the developed country and cause of chronic disability, and may cause joint failure. Osteoarthritis of knee joint is common musculoskeletal problem nowdays in Indian population. OA is ranked among top 20 diseases in 40-45 years of age group. Purpose: Osteoarthritis is responsible for major burden of disease and is one of the leading causes of global disability. People delay accessing health care for Knee Osteoarthritis due to lack of knowledge. Therefore early diagnosis, signs and symptoms and awareness to delay/stall future disease process is essential for enhancing quality of life and function. Objective: Objective of this study was to assess the knowledge about preventive measures of OA. Setting: Different parts of Pune city. Method: The study is undertaken to assess knowledge about preventives measures of OA with the help of self-made questionnaire in urban population of Pune, approved by ethical committee. The study was commenced with a sample size of 200 among urban residents of Pune. The questionnaire was circulated via google form through mails and other social media platforms and data analysis was done. Participants: Age group 25-50 years were taken for the study. Population who were clinically pre diagnosed with osteoarthritis were excluded from the study. Outcome measure: The primary outcome measure of this study was to assess the knowledge about preventive measures of OA where they have good knowledge about OA. Results: Maximum population is aware about Osteoarthritis Conclusion: There is lack of awareness regarding, signs and symptoms like crepitus, deformity, tenderness, joint enlargement, and risk factors, including age, trauma, family history and also the importance of posture correction and environmental awareness. Most of the population are not aware of surgical and conventional treatment for osteoarthritis. Keywords: Osteoarthritis, prevention, bones and joints, awareness

Crowdsourced mapping of tourism distribution and dynamics in Iceland using Flickr

ABSTRACT Social media has become a catalyst for tourism in nature-based destinations, raising management challenges to cope with abrupt visitation growth in sensitive settings. Access to location-based social media data provides valuable material to identify and monitor spatial distribution patterns in tourism visitation and longitudinal trends, especially in vast, remote settings. This paper aims to evaluate the use of Flickr geodata for mapping spatiotemporal dynamics of tourist behaviors in Iceland. The consistency of the Flickr dataset with other proxies supports its validity and relevance. Tourism visitation appears closely related with accessibility factors, which are characterized by a strong seasonal variation. On longer timescales, the data reflect the increase of off-season visitation and the emergence of new tourist sites. This study demonstrates the value and potential of such a dataset to get an overview of the spatial patterns and dynamics of tourism at a national-level and outlines the limits inherent to its use.

Cohort profile: The Social media, smartphone use and Self-harm in Young People (3S-YP) study-A prospective, observational cohort study of young people in contact with mental health services.

The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the mechanisms underpinning associations between social media and smartphone use and self-harm in a clinical youth sample. We present here a comprehensive description of the cohort from baseline data and an overview of data available from baseline and follow-up assessments. Young people aged 13-25 years were recruited from a mental health trust in England and followed up for 6 months. Self-report data was collected at baseline and monthly during follow-up and linked with electronic health records (EHR) and user-generated data. A total of 362 young people enrolled and provided baseline questionnaire data. Most participants had a history of self-harm according to clinical (n = 295, 81.5%) and broader definitions (n = 296, 81.8%). At baseline, there were high levels of current moderate/severe anxiety (n = 244; 67.4%), depression (n = 255; 70.4%) and sleep disturbance (n = 171; 47.2%). Over half used social media and smartphones after midnight on weekdays (n = 197, 54.4%; n = 215, 59.4%) and weekends (n = 241, 66.6%; n = 263, 72.7%), and half met the cut-off for problematic smartphone use (n = 177; 48.9%). Of the cohort, we have questionnaire data at month 6 from 230 (63.5%), EHR data from 345 (95.3%), social media data from 110 (30.4%) and smartphone data from 48 (13.3%). The 3S-YP study is the first prospective study with a clinical youth sample, for whom to investigate the impact of digital technology on youth mental health using novel data linkages. Baseline findings indicate self-harm, anxiety, depression, sleep disturbance and digital technology overuse are prevalent among clinical youth. Future analyses will explore associations between outcomes and exposures over time and compare self-report with user-generated data in this cohort.

“We Are Detective”: Transvestigations, conspiracy and inauthenticity in ‘gender critical’ social media discourses

This article explores the socio-behavioral phenomenon of “transvestigations” in social media discourses. This phenomenon is most often characterized by users referring – erroneously – to apparent physiological cues of one's assigned sex at birth, which motivate their interpretation of one's transgender status, particularly among those who do not identify as transgender and focus primarily on cisgender celebrities. It is difficult to know whether these behaviors and interactions are symptomatic of an authentic “gender critical” ideology or inauthentic practices that only fan the flames of antagonism in an ongoing social struggle. In this paper, I explain how transvestigations are primarily based on transphobic “gender critical” discourses and are fundamentally underpinned by a conspiracy of transgender ubiquity. I also explain how transgender users and their allies troll transvestigators by mocking the same discourses and assumptions. To do so, I analyze social media data using methods from cultural political economy and social-cognitive discourse studies. I argue that the possibilities and uses of social media for misinformation, conspiracy, and mimetic antagonism facilitate a discursive landscape in which authentic "gender-critical" discourses are indistinguishable from potentially inauthentic conspiratorial discourses. By highlighting how social media and its users (re)construct a world without distinction between authenticity and inauthenticity, this article raises questions about the im/possibilities of change in contexts of ongoing and ever-increasing antagonisms.

Patient Information Needs and Decision-Making Before a Cardiac Implantable Electronic Device: A Qualitative Study Utilizing Social Media Data.

The decision to receive a cardiac implantable electronic device (CIED) represents a challenging experience for patients. However, the majority of previous research has only considered retrospective accounts of patient experiences. This study aimed to use social media data to characterize the information sought by people anticipating or considering CIED implantation and factors that influence their decision-making experiences. A Python-based script was used to collect posts made to a community intended for discussions concerning CIEDs on the social media platform Reddit. Reflexivecontent analysis was used to analyze the collected data. From 799 posts collected, 101 made by 86 participants were analyzed. The reported median (range) age of participants was 34 (16-67), and most were anticipating or considering a pacemaker. Three overarching categories classified the data: "Use of social media to meet informational and other needs"; "Factors influencing acceptance of the need for implantation"; and "Specific concerns considered during decision-making." Participants anticipating or considering a CIED predominantly sought experiential information. Among asymptomatic participants, doubts were prevalent, with acceptance being an influential factor in decision-making. Healthcare professionals should recognize the informational and emotional needs of prospective CIED patients and tailor support mechanisms to better facilitate their decision-making.

Social Media Archiving in Practice: A Troubled Landscape in Review

ABSTRACT The following article provides a brief overview on the current state of institutional archival practices of social media content and explores some of the key challenges the field continues to face. Social media use has developed into the leading means of human communication in the world, with billions of people utilizing multiple platforms on a daily basis to both document their individual lives and forge digital communities around various interests. Users generate exorbitant amounts of ephemeral content in the form of text, photos, videos and more across multiple platforms which are, by design, unarchivable. As a result, libraries, archives, and other cultural preservation institutions have struggled to find the proper technological tools to capture the sheer amount of data being generated, while also grappling with a variety of methodological, ethical and legal questions that the practice raises. All the while, the amount of social media data containing potentially important cultural heritage artifacts of our time continues to grow and remain at risk of being forever lost, altered or altogether erased. This article looks at several notable examples of major social media data loss that have already taken place in recent history and examines one of the largest institutional attempts to preserve social media thus far in the Library of Congress’ Twitter Archive. By exploring some of the key challenges that arose during this attempt which ultimately grounded the project, this article aims to better understand what continues to keep the practice of social media archiving at bay, and what large scale changes might be necessary to make any further progress in the field.

Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review.

The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data. This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care. We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001). Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

Social media for palliative and end-of-life care research: a systematic review

BackgroundSocial media with real-time content and a wide-reaching user network opens up more possibilities for palliative and end-of-life care (PEoLC) researchers who have begun to embrace it as a complementary...

Image-text crisis tweet categorization:a caption-based approach

The growth of social media usage this last decade has made available a massive and valuable volume of multimedia data. However, the lack of large multimodal annotated datasets, along with the inherent noise and the diversity of multimodal relations in this type of data presents challenges for machine learning methods. Unlike classic multimodal data, social media data comes with a large diversity of relations between image and text making the interaction between the two modalities more difficult.Previous research concentrated on fusion strategies with separate encoders for each modality. This paper introduces CMB (Caption-based Multimodal BERT), a method of classifying crisis-related social media posts by utilizing information from both images and texts. CMB translates the image modality into a text-compatible space, facilitating intermodal interaction. Furthermore, CMB presents training opportunities to enhance the model's robustness to missing modalities. Experimental results show that CMB is competitive with well-established, costly, and manually crafted multimodal models.

Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling

PurposeIn recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).Design/methodology/approachLeveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.FindingsTo substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.Originality/valueThis scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

Social Media Analysis in Sudden Onset Disasters and its Usefulness for Decision Makers - Excerpt of a Scoping Review

Problem: Despite the potential of artificial intelligence for rapid social media analysis, practical implementation remains limited. Approach: We conducted a scoping review using iteratively developed search terms in two electronic databases. We then conducted a descriptive and thematic analysis to capture the key concepts, findings and methods in the literature. Results: After reviewing 130 papers, 27 papers were identified that met our inclusion criteria. Four key themes emerged, including tool development, tool performance comparison, gaining insights from social media data, and usefulness for decision makers. Conclusions: While acknowledging the importance of integrating social media into crisis and disaster management, there is a lack of empirical evidence on data reliability and preparation for decision makers. More empirical studies on social media analytics, specifically focusing on usefulness for crisis and disaster management, are recommended.