Volume Of Social Media Research Articles

Motivations, barriers, and social media volume usage influence on exercise

Exercise contributes to a healthy lifestyle. Nonetheless, many people are insufficiently active. This study investigated exercise motivation, barriers to exercise, and the impact of social media volume usage among 18-25 year olds from a Midwestern university (USA). Data was collected via survey. Overall, the sample noted the psychological and fitness sub models and the positive health and strength and endurance subscales to be most important. There was a significant difference between college athletes and nonathletes for two sub models and seven subscales. College athletes noted time constraints and nonathletes noted their lack of motivation and time constraints (equally) as top barriers. Overall, the primary barriers to exercise were rated as less impactful (p < .001) by the college athletes (45.3 ± 34.2) compared to nonathletes (69.2 ± 28.3). Social media usage was prevalent in our sample, and it might be differentially impacting users. Highest-volume users rated barriers to be more impactful than lowest-volume users (p = .035). Social media is a current-culture culprit that helps “not enough time” persist as a key barrier to exercise. The impacts of social media on fitness and activity need further investigation.

Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article

The assessment of academic articles is based on the number of citations, but the number only is not enough. So now there is Altmetric which can measure the impact of academic articles from the number of citations and using social media, usually Twitter. Still, the number of mentions on Twitter is not enough because the expressions of the sentences vary. Mentions must be classified according to neutral, positive, and negative criteria. Sentiment analysis is performed on tweets to measure social media volume and attention related to research findings from academic articles. There are many sentiment analysis methods, so this study aims to compare sentiment analysis methods using Decision Tree, K-NN, Naïve Bayes, and Random Forest to get the most suitable methods. The evaluation method in this study uses the Confusion Matrix by searching for Accuracy, Precision, and Recall values. The results show that the most suitable sentiment analysis method is Naïve Bayes by obtaining the highest classification suitability value of the other methods, which has an actual positive sentiment value of neutral 2056, positive 1200, and negative 1292. In addition, Naïve Bayes gets the highest accuracy score of 95, 45%.

Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genread hocpipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it)beforethe misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking—a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading.

Temporal positional lexicon expansion for federated learning based on hyperpatism detection

AbstractInternet‐based information exchange has resulted in the propagation of false and misleading information, which is highly detrimental to individuals and humankind. Due to the speed and volume of social media news production, supervised artificial intelligence algorithms require many annotated data, which is difficult, costly, and time‐consuming. To address this issue, we offer a novel federated semi‐supervised framework based on self‐ensembling that utilizes the linguistic and stylometric information of annotated news articles and searches for hidden patterns in unlabeled data to denoise labels. Self‐ensembling predicts the labels of unlabeled data by using the outcomes of network‐in‐training from earlier epochs. These cumulative predictions should be a stronger predictor for unknown labels than the output of the most recent training epoch; hence, they may be utilized as a substitute for the labels of unlabeled data. The approach is distinctive in collecting all of the outputs from the neural network's past training periods. It utilizes them as an unsupervised target against which to assess the current output prediction of unlabeled articles. We intend to create a dataset centred on denoising to forward the study. The dataset is mapped using (1) the shifting focus time from published news articles and (2) the semi‐supervised method based on coincidence contexts for a neural contrast embedding model for learning low‐dimensional continuous vectors that generate a focus time‐based query in sequential news articles for temporal comprehension. The model achieved 0.83% F‐measure with lexicon expansion semi‐supervised learning.

Digital platforms in the news industry: how social media platforms impact traditional media news viewership

ABSTRACT We examine how social media plays the role of an attention driver for traditional media. Social media attracts and channels attention to a topic. This attention triggers people to seek further information that is reported professionally in traditional media. Specifically, the volume of social media posts about a stock influences the attention to this stock the next day, proxied by the viewership of news articles on the same stock published the next day. We test this hypothesis in the stock market context because social media is less likely than traditional media to diffuse fundamental information in the stock market. Analysing stock-related news articles and stock-related social media posts from Sina Finance and Sina Weibo, we find that the social media post volume of a stock at time t-1 is associated with the traditional media viewership of the same stock at time t. This effect is amplified when social media sentiment about the stock is more intense or positive, and with an increase in the volume of verified social media posts about the stock. Our results provide evidence that social media platforms act as attention drivers, which differ from the information channel functions discussed in prior literature.

Forecasting the subway passenger flow under event occurrences with multivariate disturbances

Subway passenger flow prediction is of great significance in transportation planning and operation. Special events, as for vocal concerts and sports games, lead large-scaled passenger flow with few periodic trends. Therefore, predicting subway outbound flow during events is a challenging task. In recent years, social media has been used for socio-economic forecasting. Correlation analysis shows that the trend of social media volume can be used for passenger flow prediction under events occurrences. In this paper, besides traditional smart card data, we incorporate social media data into passenger flow prediction. The multivariate disturbance-based hybrid deep neural network (MDB-HDNN), which models the disturbances of the inbound flow from the nearby stations and the social media post trends, is proposed for subway passenger flow prediction during events. Experimental results on three real-world datasets demonstrate that the MDB-HDNN performs well under various settings and has better robustness. Our findings and results can provide decision support for schedule formulation and passenger flow guidance.

Speaking Up on Black Lives Matter: A Comparative Study of Consumer Reactions toward Brand and Influencer-Generated Corporate Social Responsibility Messages

The Black Lives Matter (BLM) movement ignited divergent social media reactions and conversations. Addressing the importance of message sources, this study aims to (1) compare audience reactions toward BLM content posted by brands and by influencers with brand sponsorship and (2) apply computational methods in influencer marketing to examine a large volume of social media texts qualitatively and quantitatively. A total of 32,702 comments on 110 Instagram posts on BLM were collected and analyzed through both human efforts and machine-learning algorithms. As a result, we found that black-influencer-created BLM attracted higher consumer engagement than posts by nonblack influencers and brands. Moreover, we generated seven different themes among which brands received the highest percentages of criticism, demands for more proactive actions, and purchase/boycott intentions. Influencers had more comments that reflected personal stories and emotion regarding BLM. Black influencers in particular received the highest percentage of praise and appreciation. Finally, comments on brands’ BLM posts embedded the highest proportion of negative sentiment, while those of black influencers’ posts were predominantly positive. Therefore, the comparisons of BLM sources elucidate the promising potential of influencers in communicating corporate social responsibility (CSR) messages of racial equality—a topic which engenders high relevance to every individual in our society.

Association of Social Media Use on Sleep Quality and Performance Among Collegiate Athletes

Objective (1) To examine the effect of social media (SM) use on sleep quality and (2) to determine the effect of SM on competition performance among college athletes. Design Cross-sectional study. Setting Two local Division III colleges, MA, USA. Participants National Collegiate Athletic Association Division III swim and track and field athletes. Intervention Data were collected onsite through an SM use form, the lifestyle survey, and the Patient-Reported Outcomes Measurement Information System Sleep Disturbance Questionnaire. Participants were divided into 3 groups based on their SM volume: (1) less active SM users (≤2 h/d), (2) moderately active SM users (>2-≤5 h/d), and (3) active SM users (>5 h/d) for analysis. Main outcome measures Sleep quality and sport performance data collected through publicly available competition results. The independent variable, SM volume (hours of SM use per day over 7 days), was collected using the iPhone screen time function. Results The cohort consisted of 40 male track and field athletes and 47 female track and field (37, 79%) and swimming (10, 21%) athletes. Mean SM use was 4.6 ± 3.4 h/d. The sleep quality of active SM users was worse than less active SM users (P = 0.033). There was an independent association between increased Facebook time and poor performance after adjusting confounders (adjusted odds ratio: 0.990, 95% confidence interval: 0.981-0.999, P = 0.049). Conclusions High SM use has a negative impact on sleep quality and may hinder competition performance among college athletes. These findings may have implications for developing SM use guidelines for college athletes to improve their sleep quality and performance.

Association of Social Media Use on Sleep Quality and Performance Among Collegiate Athletes.

(1) To examine the effect of social media (SM) use on sleep quality and (2) to determine the effect of SM on competition performance among college athletes. Cross-sectional study. Two local Division III colleges, MA, USA. National Collegiate Athletic Association Division III swim and track and field athletes. Data were collected onsite through an SM use form, the lifestyle survey, and the Patient-Reported Outcomes Measurement Information System Sleep Disturbance Questionnaire. Participants were divided into 3 groups based on their SM volume: (1) less active SM users (≤2 h/d), (2) moderately active SM users (>2-≤5 h/d), and (3) active SM users (>5 h/d) for analysis. Sleep quality and sport performance data collected through publicly available competition results. The independent variable, SM volume (hours of SM use per day over 7 days), was collected using the iPhone screen time function. The cohort consisted of 40 male track and field athletes and 47 female track and field (37, 79%) and swimming (10, 21%) athletes. Mean SM use was 4.6 ± 3.4 h/d. The sleep quality of active SM users was worse than less active SM users ( P = 0.033). There was an independent association between increased Facebook time and poor performance after adjusting confounders (adjusted odds ratio: 0.990, 95% confidence interval: 0.981-0.999, P = 0.049). High SM use has a negative impact on sleep quality and may hinder competition performance among college athletes. These findings may have implications for developing SM use guidelines for college athletes to improve their sleep quality and performance.

THE IMPACT OF SOCIAL MEDIA USE ON SLEEP QUALITY AND PERFORMANCE AMONG COLLEGIATE ATHLETES

Background:Social media (SM) use among young adults has increased significantly in recent years and has been linked to poor sleep quality. College athletes pose a unique risk for poor sleep given their many demands. Currently, limited research is available on the impact of SM use on sleep quality, or the competition performance of college athletes.Hypothesis:College athletes who use more SM will have worse sleep quality and worse competition performance.Methods:A cross-sectional study design was employed among local NCAA Division III athletes. Data was collected via a SM use form, lifestyle survey, and PROMIS sleep disturbance questionnaire. SM volume (hours of SM use per day over 7 days) was recorded using the iPhone screen time function. The main outcome measures were sleep quality, using the PROMIS T-score, and performance, collected through publicly available competition results. Participants were divided into 3 groups based on their SM volume; 1) moderate SM users (SM time ≤ 2hours/day), 2) active SM users (SM time < 2 - ≤5hours/day), and 3) super active SM users (SM time > 5hours/day). For sleep quality, an ANCOVA was used to identify differences in the 3 groups while controlling for covariates (i.e. injury status, caffeine use, number of roommates, academic load, catastrophic life event, life balance and sleep medication use) that might impact sleep and performance. For performance, a binary logistic regression model was used to determine the independent association between each variable and competition performance after adjusting for confounders.Results:87 athletes (age:19.5±1.2 years, 40 males, 47 females) completed the survey. All males participated in track and field, while the females consisted of 37 track athletes and 10 swimmers. Controlling for the effect of lifestyle variables, a difference in sleep quality was identified among the three groups (p=0.025), (Table 1. A pairwise comparison indicated that the sleep quality of super active SM users was worse than moderate SM users (p=0.033) (Figure 1). For competition performance, an independent association was found between increased Facebook time and poor performance after adjusting confounders (aOR: 0.989, 95%CI: 0.978-0.999, p=0.038), (Table 2,3).Conclusion:High SM use appears to have a negative impact on sleep quality. Also, greater SM use may hinder competition performance among college athletes. These findings may have implications for developing SM use guidelines for college athletes to improve their sleep quality and performance.Tables/Figures:Figure 1.Pairwise Comparison of Sleep Quality and Social Media use

A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles

Internet-based information circulation has given rise to the proliferation of fake and misleading contents, which has extreme hostile effects on individuals and humanity. Supervised artificial intelligence techniques require a huge amount of annotated data which is a time-consuming, expensive and laborious task as the speed and volume of social media news generation is very high. To counter this situation, we propose an innovative Convolutional Neural Network semi-supervised framework built on the self-ensembling concept to take leverage of the linguistic and stylometric information of annotated news articles, at the same time explore the hidden patterns in unlabelled data as well. Self-ensembling provides consensus predictions of the labels of unannotated data using previous epochs outputs of network-in-training. These accumulated ensemble predictions are supposed to be a better predictor for the unknown labels than the output of most recent training epoch, thus suitable to be used as a proxy for the labels of unannotated data. The uniqueness of the framework is that it ensembles all the outputs of previous training epochs of the neural network to use them as an unsupervised target for comparing them with current output prediction of unlabelled articles. The framework is validated with extensive experiments on three datasets for different proportions of labelled and unlabelled data. It can achieve highest 97.45% fake news classification accuracy using 50% labelled articles on Fake News Data Kaggle dataset. Contemporary baseline methods are placed in juxtaposition with the proposed architecture which demonstrates the robustness of our work compared to the state-of-the-art.

Interdependent relation between earned media and TV ratings

PurposeSocial media (e.g., e-WOM) and traditional media (e.g., media coverage) serve different roles in a firm's marketing activities and also interact with each other, which in turn affect the market outcome. In addition, how market outcome affects the two types of media in turn has not been examined, which brings the need for a holistic framework. The rare study that examines this relation mostly relies on the volume of media rather than the valence. This study examines the interdependent relation between the volume and valence of social media, the volume of traditional media and TV ratings.Design/methodology/approachForty-one South Korean TV drama shows from October 2014 to March 2016 were analyzed using the 3SLS estimation to examine the interdependent relation between the variables.FindingsFirst, the volume of traditional media has a negative effect on the volume of social media. Second, ratings negatively affect the valence of social media. Third, the volume of traditional media is found to have a negative effect on ratings. This is explained by the displacement effect.Originality/valueThis study is one of the very few studies that examine the interdependent relation between various earned media and market outcomes in one framework. In addition, it has originality in that it considers the valence of social media, which is an important dimension in analyzing earned media. Our results show negative effects of news media on TV ratings and e-WOM, which diverge from common intuition.

Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data.

BackgroundThe COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being.ObjectiveThis study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic?MethodsWe analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool.ResultsLDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March.ConclusionsOur findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Empirical Analysis Тowards the Effect of Social Media on Cryptocurrency Price and Volume

Bitcoin’s value is highly dependent on the communities that use it. This network effect is true for all new technologies. Today’s online communities are so large in population that both the Facebook user and Youtuber populations have surpassed the Chinese population. We take a big data approach using millions of samples of posts from Twitter, Telegram, and Reddit to study how and if social media platforms, the epitome of online communities, affect Bitcoin’s price and volume as well as the price and volume of fifteen other top cryptocurrencies. We work in collaboration with Solume, a data centered fin-tech startup, as well as with Sentistrength, an opinion mining tool developed by researchers in the UK, to classify the sentiment of the millions of posts we study. We collected millions of posts related to 16 cryptocurrencies from November 2017 through August 2018 on an hourly basis and explore social media volume sentiment effect on these cryptocurrencies. Findings confirm that volumes of exchanged posts may predict the fluctuations of Bitcoin’s price but mainly, they predict volume. We also find that Reddit and Telegram posts have greater impact on Bitcoin volume than Twitter. Results indicate that information about the use of social media platforms can assist in tracking real world behavior and may even predict real financial market trends.

Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?

User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment.

How Social Media Impacts Mass Media News Viewership in the Stock Market

We explored in this study the impact of social media on the viewership of mass media news in the stock market. We argue that the popularity of a topic on social media can affect the viewership of news articles the next day on this same topic. Moreover, the detailed characteristics of how social media information is written can amplify this effect. Analyzing 1.4 million pieces of stock-related news and around 50 million stock-related social media posts for the years of 2013 and 2014 from Sina Finance and Sina Weibo, we found social media volume on a stock at time t-1 is associated with mass media news viewership on the same stock at time t; this effect increases when social media sentiment is more intense or more positive, or when the social media information source is more verifiable. Furthermore, we conducted causality tests to confirm the impact of social media on mass media news viewership. Our findings have implications for theory and practice, especially with respect to the design of news recommendation systems as well as recent policy and associated legislation that is based on an oversimplifying assumption that social media steals from the mass media audience.

The people speak: social media on euthanasia/assisted dying

In New Zealand, aiding and abetting a person to commit suicide or euthanasia even with consent is unlawful. The introduction of a third Bill on assisted dying to the House...

The 2016 U.S. Presidential Election and Transition Events: A Social Media Volume and Sentiment Analysis

ABSTRACTThe present study examines social media behavior as indicative of basking in reflected glory (BIRGing), the psychological process of associating with the successes of others, and cutting off reflected failure (CORFing), disassociating with the failure of others, during the 2016 presidential election, inauguration events, and subsequent worldwide Women’s March. The study harvested 10,973,629 tweets in the days leading up to and following these events. The results indicate the presence of BIRGing and CORFing processes that were likely accelerated due to the election’s outcome. While BIRG was uncovered during the election and inauguration, opponents were basking in reflected failure (BIRF) during the Women’s March by protesting the inauguration as a form of resistance. We discuss the theoretical implications and areas for future research.

Visual understanding by mining social media: recent advances and challenges

With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas such as multimedia, computer vision, and pattern recognition. Valuable auxiliary resources available on social websites, such as user-provided tags, aid in the tasks of visual understanding. Therefore, several methods have been proposed for exploring the auxiliary resources for tag refinement, image retrieval, and media summarization. This work conducts a comprehensive survey of recent advances in visual understanding by mining social media in order to discuss their merits and limitations. We then analyze the difficulties and challenges of visual understanding followed by several possible future research directions.

Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver.

The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime‐related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non‐game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary‐based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft‐from‐vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.