Platform Twitter Research Articles

Public Perspective on Hyperlipidemia Drugs and Sentiments About Hyperlipidemia on Twitter

Hyperlipidemia is a non-communicable disease (NCD) caused by several factors, such as a person's socioeconomic status, culture, customs, habits, and lifestyle. Through user interaction on social media, we can discover the model anti-hyperlipidemia by extracting information, complaints, suggestions, and calls for help about the treatment, which will play a role as an intervention to reduce hyperlipidemia in Indonesia. This study aimed to identify factors influencing perceptions of hyperlipidemia drugs and resulting sentiment on the social media platform Twitter. This study used user-uploaded tweet data to compare perceptions of hyperlipidemia drugs in 2020 and keywords for hyperlipidemia terms and medicine. Tweets related to anti-hyperlipidemia were extracted by issuing tweets containing advertisements, news, re-tweet, and content outside of health. The tweet data obtained was then carried out through content analysis, including point of view, theme, and sentiment analysis, to identify whether the resulting tweets are positive, neutral, or negative using the Support Vector Machine (SVM) method. We identified 1572 hyperlipidemia-related tweets and 153 specific tweets describing hyperlipidemia medications. Tweets about anti-hyperlipidemia showed 99 tweets from the first-person perspective, 23 from the second-person perspective, 22 from healthcare professionals, and nine unidentifiable (other). Sixty-three tweets talked about the benefits of lipid-lowering drugs, 17 complaint tweets, 49 suggestion tweets, 17 question tweets, and two side effect tweets. Assessing public perceptions and sentiment toward hyperlipidemia treatment can be used to develop strategies to increase treatment adherence, improve treatment outcomes, and target health promotion efforts.

Detection of offensive terms in resource-poor language using machine learning algorithms.

The use of offensive terms in user-generated content on different social media platforms is one of the major concerns for these platforms. The offensive terms have a negative impact on individuals, which may lead towards the degradation of societal and civilized manners. The immense amount of content generated at a higher speed makes it humanly impossible to categorise and detect offensive terms. Besides, it is an open challenge for natural language processing (NLP) to detect such terminologies automatically. Substantial efforts are made for high-resource languages such as English. However, it becomes more challenging when dealing with resource-poor languages such as Urdu. Because of the lack of standard datasets and pre-processing tools for automatic offensive terms detection. This paper introduces a combinatorial pre-processing approach in developing a classification model for cross-platform (Twitter and YouTube) use. The approach uses datasets from two different platforms (Twitter and YouTube) the training and testing the model, which is trained to apply decision tree, random forest and naive Bayes algorithms. The proposed combinatorial pre-processing approach is applied to check how machine learning models behave with different combinations of standard pre-processing techniques for low-resource language in the cross-platform setting. The experimental results represent the effectiveness of the machine learning model over different subsets of traditional pre-processing approaches in building a classification model for automatic offensive terms detection for a low resource language, i.e., Urdu, in the cross-platform scenario. In the experiments, when dataset D1 is used for training and D2 is applied for testing, the pre-processing approach named Stopword removal produced better results with an accuracy of 83.27%. Whilst, in this case, when dataset D2 is used for training and D1 is applied for testing, stopword removal and punctuation removal were observed as a better preprocessing approach with an accuracy of 74.54%. The combinatorial approach proposed in this paper outperformed the benchmark for the considered datasets using classical as well as ensemble machine learning with an accuracy of 82.9% and 97.2% for dataset D1 and D2, respectively.

Clustering Content Types and User Roles Based on Tweet Text Using K-Medoids Partitioning Based

In this modern era, the spread of information occurs rapidly through social media. One of the channels for disseminating information is through the Twitter platform. Many Twitter users respond to existing content with positive, negative and neutral responses. One of the hot content to respond to is political content. This content is currently being discussed considering the approaching election of the 2024 Presidential Candidate of the Republic of Indonesia. One of the candidate pairs discussed was Anies Baswedan. With so many responses from Twitter users, it will be difficult to track whether users support Anies Baswedan to run as a presidential candidate due to the large number of responses. This study aims to determine the response of twitter users to the advancement of Anies Baswedan as a presidential candidate. The method used in this study is the K-Medoids Partitioning-Based algorithm based on twitter user text. This algorithm was chosen because it is easy to implement considering the basis of K-Medoids development is the K-Means algorithm but the K-Medoids algorithm can overcome the shortcomings of the K-Means algorithm which is sensitive to outliners. The evaluation will be done using Silhouette Score which produces a value of 0.35 with the number of clusters is 2. Then an analysis of each cluster is carried out by looking at the words in the cluster. As a result, from the two clusters formed, both clusters contain positive content and show that Twitter users support Anies Baswedan to run as a 2024 presidential candidate.

Investigating the effectiveness of Twitter sentiment in cryptocurrency close price prediction by using deep learning

AbstractIn recent years, cryptocurrencies' price prediction has attracted the interest of many people including investors, researchers and practitioners. In this study, we proposed a hybrid model for predicting the daily close price of cryptocurrencies based on different neural networks such as long short‐term memory, convolutional neural network and attention mechanism. Using an ensemble of three pre‐trained language models, we extracted sentiment of cryptocurrency‐related tweets posted between 1 January 2021 and 31 December 2021. We constructed 20 different versions of our model and evaluated their performance on data of 27 most traded cryptocurrencies using a history of previous days' sentiment data along with close prices as input data. The flexible input layer of our model enables different ways of feeding data into the model to adjust it for different cryptocurrencies to obtain better predictions. Our analysis revealed several important findings. We showed that longer sequences of input data achieve most accurate predictions on average. More specifically, using a history of 14‐ and 21‐days' data results in lowest RMSE values on average compared to using a history of 7 days. However, there is no significant difference between the results related to the input sequences with lengths of 14 and 21. In addition, our findings suggest that sentiment data can be useful in predicting prices for more than 70% of the studied cryptocurrencies. Thus, peoples' emotions, opinions, and sentiment that are expressed through their posts on Twitter platform play a significant role in prediction of cryptocurrencies' prices.

Social Media Zero-Day Attack Detection Using TensorFlow

In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of risks are referred to as zero-day attacks since no pre-existing anti-malware measures are available to mitigate them. Consequently, significant damages occur when vulnerabilities in systems are exploited. The effectiveness of security systems, such as IPS and IDS, relies heavily on the prompt and efficient response to emerging threats. Failure to address these issues promptly hinders the effectiveness of security system developers. The purpose of this study is to analyze data from the Twitter platform and deploy machine learning techniques, such as word categorization, to identify vulnerabilities and counteract zero-day attacks swiftly. TensorFlow was utilized to handle the processing and conversion of raw Twitter data, resulting in significant efficiency improvements. Moreover, we integrated the Natural Language Toolkit (NLTK) tool to extract targeted words in various languages. Our results indicate that we have achieved an 80% success rate in detecting zero-day attacks by using our tool. By utilizing publicly available information shared by individuals, relevant security providers can be promptly informed. This approach enables companies to patch vulnerabilities more quickly.

Gendered COVID-19 discussions on Twitter: a Norwegian case

PurposeThe COVID-19 pandemic has resurfaced challenges to gender equality and gender relations both worldwide and in Norway. There have been massive public discussions on social media platforms, highlighting the potential of analysing public discourses in a non-reactive manner (Rauchfleisch et al., 2021). Further, discourses from social media may affect cultural representations and broad discourses in society (Rambukkana, 2015), such as that related to gender. In this article, by studying the Norwegian Twitter users' discussion on gender as related to COVID-19 pandemic, the authors will examine the everyday gendered discourses.Design/methodology/approachData for this project were collected from the social media platform Twitter. The authors conducted the search on 16th November 2020, and that resulted in a total of 485 results, inclusive of both original tweets and replies. The data were analysed qualitatively using thematic analysis.FindingsThe thematic analysis of the tweets revealed three main categories which were mirrored in recognisable and widespread discourses about gender: (1) stereotypical gendered behaviours, (2) construction of masculinities and (3) othering. The authors argued that the stereotypes on gendered behaviour, traits and ideology together attribute to the maintenance of unequal gender structures.Originality/valueThis article explored discourses on gender on Twitter, the networked public sphere of Norway during the COVID-19 pandemic. Given that discourses both reflect and shape social configurations, they have the power to shape gender realities. With the transcendence of social media across geographic boundaries, the authors’ findings are relevant both for Norway and globally.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2022-0482

Use of social media data, online reviews and wikipedia page views to measure visitation patterns of outdoor attractions

This study explores the suitability of activity counts extracted from social media platforms (Twitter, Flickr), review portals (TripAdvisor, Google Maps) and Wikipedia article views to model official visitor counts at selected outdoor attractions in Florida (U.S.) and Carinthia (Austria). It applies correlation analysis, multiple regression, and time series analysis to identify which of these user-generated content (UGC) sources and their combinations best match official monthly visitor count patterns for an analysis period of three years (2019–2021). With travel activities being severely hampered during 2020 due to the COVID-19 pandemic, the analysis also aims to analyze to which extent reduced visitor counts are reflected in the respective UGC sources. Results show that the number of Google Maps reviews combined with Wikipedia pageviews best explain the variability of monthly official visitor counts in Ordinary Least Squares (OLS) regression in both study areas. While the comparison analysis was conducted for monthly counts, data from some UGC sources can reflect shorter term activity fluctuations. Time series analysis detected a seasonality of 12 months for Wikipedia pageviews, Google Maps reviews, and official visitor counts for Austria due a clearly distinct summer season. As opposed to this, for Florida, due to a climate that facilitates all-year round park visitations, periodograms yielded different seasonality frequencies for UGC sources and official visitor counts. A short-term drop in visitor counts due to COVID-19 was evident in Florida both in UGC sources and official visitor counts in spring 2020, whereas for Austria attractions reduced activity is only somewhat reflected in UGC sources but not in actual visitor counts due to attractions still being closed in April. Management implicationsThis research explores multiple UGC sources for modeling official visitor counts at outdoor attractions. A combination of several sources has the advantage that it can help mitigate known limitations of UGC from individual sources, such as sparsity of geodata, data retrieval restrictions, sociodemographic bias, and varying popularity across regions. This research also revealed a better model fit of combined UGC data with official visitor counts than using any of the UGC sources alone. Data-rich UGC sources offer daily or weekly activity counts that provide a more refined temporal resolution than typical official visitor counts which are often limited to monthly data aggregations.

Communication of COVID-19 Misinformation on Social Media by Physicians in the US

Approximately one-third of the more than 1 100 000 confirmed COVID-19-related deaths as of January 18, 2023, were considered preventable if public health recommendations had been followed. Physicians' propagation of misinformation about COVID-19 on social media and other internet-based platforms has raised professional, public health, and ethical concerns. To characterize (1) the types of COVID-19 misinformation propagated by US physicians after vaccines became available, (2) the online platforms used, and (3) the characteristics of the physicians spreading misinformation. Using US Centers for Disease Control and Prevention guidelines for the prevention and treatment of COVID-19 infection during the study window to define misinformation, structured searches of high-use social media platforms (Twitter, Facebook, Instagram, Parler, and YouTube) and news sources (The New York Times, National Public Radio) were conducted to identify COVID-19 misinformation communicated by US-based physicians between January 2021 and December 2022. Physicians' state of licensure and medical specialty were identified. The number of followers for each physician on 4 major platforms was extracted to estimate reach and qualitative content analysis of the messages was performed. Outcome measures included categories of COVID-19 misinformation propagated, the number and traits of physicians engaged in misinformation propagation, and the type of online media channels used to propagate misinformation and potential reach. The propagation of COVID-19 misinformation was attributed to 52 physicians in 28 different specialties across all regions of the country. General misinformation categories included vaccines, medication, masks, and other (ie, conspiracy theories). Forty-two physicians (80.8%) posted vaccine misinformation, 40 (76.9%) propagated information in more than 1 category, and 20 (38.5%) posted misinformation on 5 or more platforms. Major themes identified included (1) disputing vaccine safety and effectiveness, (2) promoting medical treatments lacking scientific evidence and/or US Food and Drug Administration approval, (3) disputing mask-wearing effectiveness, and (4) other (unsubstantiated claims, eg, virus origin, government lies, and other conspiracy theories). In this mixed-methods study of US physician propagation of COVID-19 misinformation on social media, results suggest widespread, inaccurate, and potentially harmful assertions made by physicians across the country who represented a range of subspecialties. Further research is needed to assess the extent of the potential harms associated with physician propagation of misinformation, the motivations for these behaviors, and potential legal and professional recourse to improve accountability for misinformation propagation.

An organizational form framework to measure and interpret online polarization

ABSTRACT As social media becomes more ingrained in political and social discourse around the world, the need for systematic measures and interpretations of online polarization grows. In this work, we introduce five major organizational forms of community-level polarization and nine subforms to support comparison of community dynamics in controversial discussions across time, platforms, languages, and domains. We propose a multi-dimensional social network analysis approach to characterize the structure within and between ideologically opposed communities and evaluate the organizational form. Through three case studies, we demonstrate applying the proposed methodology on two social media platforms (Twitter and Reddit) and levels of conflict (topic and partisanship). This work has implications for depolarization interventions and policies on social media.

Psychological Study of Cyber-Bullying Against Adolescent Girls in India Using Twitter

Due to the rise in digital activity of students as well as increased social media presence, the lack of regulation of platforms has given rise to another form of bullying, popularly known as cyberbullying. Cyberbullying is one of the most adverse issues prevalent in schools nationwide. Cyberbullying refers to bullying that happens over any web-interfaced or electronic platform. It is an activity that significantly affects the mental and physical health of its victims. With increased secrecy, the frequency and propagation of cyberbullying remain high due to the information technology infrastructure available today. Understanding cyberbullying trends and preventing them, using suitable machine learning algorithms, could help numerous school students lead better lives, as well as make better decisions, which help them grow and flourish into capable future leaders. Hence, the authors' aim for this research paper is to focus on adolescent girls using various tools and techniques like text analytics and image analytics. For this paper, the authors study a sample of netizens. The location where the analysis is conducted is New Delhi, and the real-world data is extracted from Twitter in English. The real-world data is extracted using appropriate data mining algorithms to find hidden patterns and then conduct the analyses required to understand the psychology of girls and boys and the tonality and voice of the tweets/posts. This is done from the open-source information available on the platform (Twitter) from tweets by the users. There is little to no bias as the entire process can be automated; hence, tweets will be filtered or flagged based on data. Such a method allows one to get access to unbiased data. Bias, in this case, can be defined as prejudice in action and response received from a participant. The results are then analysed using polarity and subjectivity. Understanding psychology and personality traits helps in drawing insights from the expressions collected. The authors will be studying the sample bios, likes, and comments of the sample using a lexical and syntactical approach. Six thousand top tweets are extracted, and the 15 tweets which score the highest on polarity and subjectivity values are taken for further analysis. The tweets are filtered based on 16 responses from a focus group filtering the 20 most popular profane words. Since the data is extracted using Twitter (i.e., a secondary data source), the authors address the gap in current psychological analyses. In such studies, one usually circulates questionnaires to understand the participant, but, for this research though, the authors will be studying the data without bringing the concerned individual into play, thereby eliminating the human bias, which is a significant limitation of gathering responses through a questionnaire. There is increased scope for further streamlining the model. The inferences include understanding the regulation of a social media platform, the degree of aggression on the platform, and an effort to distinguish those who cause such aggression.

Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects

SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking.

Summarizing twitter posts regarding COVID-19 based on n-grams

The COVID-19 pandemic announced by the World Health Organization has disrupted human lives at different scales, including the economy, public health, and people's emotions. Social media databases record huge accumulated information concern this pandemic. Twitter platform is considered one of the most active social media that enable users to tweet in different conversations they are concerned about. The problem arises when tweeters want to search about a specific topic. They can only sort tweets by its recency to understand conversation and not by relevancy. This makes tweeters read through the most tweets to understand what was firstly discussed about the related topic. Some strategies were developed for summarizing tweets but summarizing topics of COVID-19 are still at the beginning. The current research aims to introduce a technique to present a short summary related COVID-19 topics with consuming little time and effort. Thus, summarization task started by clustering topics based on latent dirichlet allocation (LDA) method and K-means clustering and then selected the important sentences to format summarization. The study also compares bigram-based and unigram-based summarization. Different metrics were used to evaluate results and experiments at each stage, and the output of the proposal system was evaluated using ROUGE metrics.

Comparing the Discussion of Telehealth in Two Social Media Platforms: Social Listening Analysis.

Social media is used as a source of information and platform to discuss health care; however, there is little research on discussion of telehealth in social media. Past research has looked at individual platforms, but a comparison of discussion on two platforms (Reddit and Twitter) has not been performed. Understanding telehealth-related social media discourse and the differences between platforms may provide insights into how telehealth is characterized online and which platforms provide patient perspectives. The COVID-19 pandemic provides a unique case study to examine how social media users approached both Reddit and Twitter during an international health crisis. This study used natural language processing tools and two social media platforms to (1) characterize and contrast each platform's telehealth-related posts according to themes and (2) assess the frequency of telehealth and telehealth-related terms posts before and during the onset of the COVID-19 pandemic. We collected 6 years (2016 through 2021) of social media posts from Twitter and Reddit. The themes of the corpus were extracted using hashtags, subreddits, and Latent Dirichlet Allocation (LDA) and were analyzed using descriptive statistics. Both Twitter and Reddit showed exponential growth in the use of the term "telehealth" and telehealth-related terms in early 2020. The use of telehealth-related terms and discussion of COVID-19 coincided in both social media sites; however, other themes were discussed, including how to use telehealth. Reddit LDA clusters showed greatest usage of "telehealth" when associated with using or suggesting telehealth for receiving therapy, counseling, or psychoanalysis while Twitter focused on sharing telehealth news, products, and services. Twitter and Reddit had extensive growth in the use of telehealth-related terms after the COVID-19 pandemic. Twitter and Reddit showed themes connecting COVID-19 to telehealth, especially in reference to services, therapy, and counseling, however, Reddit had more discussion suggesting use of telehealth services or requesting peer insights into how to use telehealth as compared with Twitter, which appeared more focused on telehealth as a business or product.

Exploring the challenges of the COVID-19 vaccine supply chain using social media analytics: A global perspective

The Coronavirus (COVID-19) pandemic has highlighted the importance of effective vaccine supply chain management (SCM). As the world raced to curb the spread of the virus, the efficient distribution of vaccines became crucial to ending the pandemic. However, the challenges of distributing COVID-19 vaccines globally were exacerbated by the pandemic itself, leading to delays, shortages, and disruptions under disaster conditions. In this study, we use social media analytics to explore the impact of COVID-19 vaccine supply chain challenges from a global perspective. We collected data from two social media platforms—Twitter and the Global Database of Events, Language and Tone (GDELT)—between January 2020 and November 2022. The analysis is developed leveraging Natural Language Processing (NLP) techniques, such as topic modelling and sentiment analysis. We found that vaccine supply chain challenges significantly impacted the ability of healthcare systems to respond to the pandemic, with significant differences between regions (such as in the case of developing countries). Our findings highlight the need for improved coordination and collaboration in vaccine SCM to ensure equitable access. In addition, this research provides valuable practical insight into using social media analytics and its potential to enhance decision-making capabilities for policymakers and industry leaders.

The Social Responsibility of the Emirati Elites in Interactive Media: An Applied Study on the Twitter Platform

This study, entitled "The Social Responsibility of the Emirati Elites in Interactive Media: An Applied Study on the Twitter Platform", aims to identify the concept of social responsibility among the Emirati elites in interactive media, specifically Twitter, in addition to identifying the compatibility of the motives of the Emirati elites with the principles of the theory of social responsibility. (The theory on which the study is based) in this platform, and the most important contents and issues that the Emirati elites focus on the Twitter platform in terms of social responsibility, in addition to the most prominent forms of those responsibilities that the elites exercise in this aspect. This study relied on the qualitative approach, using The standardized interview as a main tool for collecting study data, which was applied to the intentional study sample, consisting of (12 individuals) from the various elite groups in the Emirati society (the study community), where the results came to bear a great importance for dealing with this type of media, as it revealed The study revealed, according to the mechanism that was used in analysing the answers and the qualitative data they bear, a set of important results, the most important of which is that the active Emirati elites are on a platform Twitter is considered, based on the principle of responsibility and patriotism, as elites who support the government's directions and policies, without any direct guidance from the government to do this support, and this reinforces the fact that the UAE 2022 won the people's confidence in the government by 87% and ranked second in the world, as it was found that the majority of elites in different specializations care about issues Social, and they are always looking for solutions to what concerns the people, as it turned out that "Twitter" is an important source of change, influence and formation of public opinion, and that it is one of the most important interactive and direct official platforms between the government and the people, and the presence of government leaders on the Twitter platform and with continuous interaction, increased The extent of responsibility on the effective and influential elites, and the concept of social responsibility is considered the most widespread and repeated concept during the analysis stage, in addition to the compatibility of the study with the second principle of the theory of social responsibility, which is concerned with objectivity, balance and accuracy, and that social responsibility is considered the necessary requirement for the elites towards community awareness and development. Keywords: elite social responsibility, interactive media - Twitter.

Consensus on community guidelines: an experimental study on the legitimacy of content removal in social media

The popularization of social media has led to a considerable increase in the importance of discursive expressions of violence, especially when directed at vulnerable communities. While social media platforms have created rules to regulate such expressions, little information is available on the perception of the legitimacy of these rules in the general population, regardless of the importance of the former for the latter. It is therefore the objective of this study to analyze the perception of the seriousness of such content and the degree to which the population has established a consensus on the withdrawal of restricted discursive behaviour on three major social media platforms (Facebook, Instagram and Twitter). For this purpose, 918 participants were immersed in an experimental paradigm in three different groups (n1 = 302; n2 = 301; n3 = 315). Each was presented with stimuli containing discursive behaviour that is banned by community guidelines. The stimuli were presented differently to each group (i.e., description of the banned behaviour, description and accompanying example, example only). Our experimental data reveals that the degree of consensus on the need to remove content is quite high, regardless of the style of presentation. It furthermore suggests that the behaviour in question is perceived as very serious, due to the harm that our participants presume it to cause. These results have important implications for the debate on freedom of expression on the Internet and its regulation by private actors.

Sentiment Analysis of Twitter Discussions on Rafael Alun: Multinomial Naïve Bayes and Decision Tree Approach

Political events in Indonesia often draw significant attention on the social media platform Twitter, with netizens using the medium to express their opinions and feelings. One such viral topic on Twitter revolves around Rafael Alun, a former Director General of Taxation at the Indonesian Ministry of Finance who has been implicated in a possible gratification scandal and subsequently investigated by the Corruption Eradication Commission (KPK). As a result, Rafael Alun's name has been trending on Twitter. By using sentiment analysis, it is possible to identify the prevailing sentiment elements within tweets related to Rafael Alun. The application of Multinomial Naïve Bayes and Decision Tree algorithms serves to determine the accuracy of sentiment analysis results derived from Twitter tweets data. The research process involves steps such as data pre-processing, data processing, classification and evaluation. The sentiment analysis of tweets containing the mention of "Rafael Alun" shows that the majority of the tweets express a negative sentiment. The accuracy rates obtained by implementing the Multinomial Naïve Bayes and Decision Tree algorithms are 77% and 72% respectively. It is worth noting that these percentages are relatively moderate due to the unbalanced distribution of positive, negative and neutral sentiments on the topic.

ANALISIS SENTIMEN PELAKSANAAN KULIAH ONLINE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

This writing aims to assess the satisfaction of students regarding the implementation of online lectures, categorized into three classes: positive, neutral, and negative. Data collection was conducted using Twint from the social media platform Twitter, with a total of 25,000 tweets. The data processing process to determine sentiment analysis utilized the support vector machine algorithm. With this algorithm, the obtained results show an accuracy rate of 76.86% for positive sentiment. The precision is 0.49, recall is 0.53, and the F1 score is 0.51

Impact of a Digital Strategy Team in an Academic Stroke Journal: 1 Year in Review

Background The social media platform Twitter has increasingly been leveraged to disseminate clinical and academic content, including scientific research. Launched in November 2021 as an exclusively online open access journal, the Stroke: Vascular and Interventional Neurology journal was not indexed on PubMed for its first year of publication. We aimed to evaluate the impact of the journal's Twitter presence by tracking social media posts and article metrics over the first year since the journal's inception. Methods Measures of Twitter influence from the @SVINJournal account were associated with the academic impact of articles published in the associated journal during the first 13 months of the journal's publication record. Descriptive statistics and the Pearson correlation coefficient were used to quantify measures of association, with the primary outcome being unique article requests (downloads). Results There were 174 unique articles published during the study period, with 367 unique tweets. Articles referenced in tweets from @SVINJournal had nearly twice as many requests as articles without tweets (median 967 [interquartile range, 683–1357] versus 497 [interquartile range, 331–711]; P <0.01), with a mild correlation between number of tweets and article requests when @SVINJournal cited the article in tweets ( r =0.20; P =0.009). There was a fair correlation between article request counts and any Twitter mentions ( r =0.41; P <0.001), and a poor correlation between article requests and altmetric score ( r =0.15; P =0.04). There was a small correlation between the number of citations and number of tweets from @SVINJournal ( r =0.21; P =0.006). Conclusions There was a statistically significant, but small, association between tweets and article requests as well as citations. Highly tweeted articles had a higher number of citations. In the absence of PubMed indexing and print distribution, social media platforms can have an impact in promoting peer‐reviewed content and may increase content access and citations.

Ethical concerns about social media privacy policies: do users have the ability to comprehend their consent actions?

ABSTRACT Social media platforms capture and trade consumer data for analysis, user profiling and for sale to interested parties and is used extensively in marketing. To collect, store, process and resell this data, they are legally required to obtain informed consent. However, users may agree to consent without the ability to comprehend the consequences of what that consent means. In this article we examine the complexity of privacy policies and raise ethical concerns about the ability of users to comprehend their consent actions. Using readability scores and reading fluency instruments, we analyzed the accessibility of privacy policies from a major social media platform (Meta) and a smaller platform (Twitter). Findings indicate that due to reading fluency and document length it is unlikely all users, especially minors, can authorize the consent actions which raises ethical concerns. Practical implications for managers and policy makers are also discussed and regulators may need to review users’ access to platforms where they lack the ability to comprehend their consent actions.