Showing papers on "Social media published in 2019"

Fake news on Twitter during the 2016 U.S. presidential election.

Abstract: The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.

Vaping versus JUULing: how the extraordinary growth and marketing of JUUL transformed the US retail e-cigarette market.

Abstract: Background While national surveys showed declines in e-cigarette use in the USA between 2015 and 2016, recent reports indicate that JUUL, a sleekly designed e-cigarette that looks like a USB drive, is increasingly being used by youth and young adults. However, the extent of JUUL’s growth and its marketing strategy have not been systematically examined. Methods A variety of data sources were used to examine JUUL retail sales in the USA and its marketing and promotion. Retail store scanner data were used to capture the retail sales of JUUL and other major e-cigarette brands for the period 2011–2017. A list of JUUL-related keywords was used to identify JUUL-related tweets on Twitter; to identify JUUL-related posts, hashtags and accounts on Instagram and to identify JUUL-related videos on YouTube. Results In the short 3-year period 2015–2017, JUUL has transformed from a little-known brand with minimum sales into the largest retail e-cigarette brand in the USA, lifting sales of the entire e-cigarette category. Its US$150 million retail sales in the last quarter of 2017 accounted for about 40% of e-cigarette retail market share. While marketing expenditures for JUUL were moderate, the sales growth of JUUL was accompanied by a variety of innovative, engaging and wide-reaching campaigns on Twitter, Instagram and YouTube, conducted by JUUL and its affiliated marketers. Conclusions The discrepancies between e-cigarette sales data and the prevalence of e-cigarette use from surveys highlight the challenges in tracking and understanding the use of new and emerging tobacco products. In a rapidly changing media environment, where successful and influential marketing campaigns can be conducted on social media at little cost, marketing expenditures alone may not fully capture the influence, reach and engagement of tobacco marketing.

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.

Abstract: Social data in digital form—including user-generated content, expressed or implicit relations between people, and behavioral traces—are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding “what the world thinks” about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naive usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. “For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated.” –Ursula Franklin1

MVAE: Multimodal Variational Autoencoder for Fake News Detection

Abstract: In recent times, fake news and misinformation have had a disruptive and adverse impact on our lives. Given the prominence of microblogging networks as a source of news for most individuals, fake news now spreads at a faster pace and has a more profound impact than ever before. This makes detection of fake news an extremely important challenge. Fake news articles, just like genuine news articles, leverage multimedia content to manipulate user opinions but spread misinformation. A shortcoming of the current approaches for the detection of fake news is their inability to learn a shared representation of multimodal (textual + visual) information. We propose an end-to-end network, Multimodal Variational Autoencoder (MVAE), which uses a bimodal variational autoencoder coupled with a binary classifier for the task of fake news detection. The model consists of three main components, an encoder, a decoder and a fake news detector module. The variational autoencoder is capable of learning probabilistic latent variable models by optimizing a bound on the marginal likelihood of the observed data. The fake news detector then utilizes the multimodal representations obtained from the bimodal variational autoencoder to classify posts as fake or not. We conduct extensive experiments on two standard fake news datasets collected from popular microblogging websites: Weibo and Twitter. The experimental results show that across the two datasets, on average our model outperforms state-of-the-art methods by margins as large as ~ 6% in accuracy and ~ 5% in F1 scores.

Sentiment Analysis in Tourism: Capitalizing on Big Data:

Abstract: Advances in technology have fundamentally changed how information is produced and consumed by all actors involved in tourism. Tourists can now access different sources of information, and they can generate their own content and share their views and experiences. Tourism content shared through social media has become a very influential information source that impacts tourism in terms of both reputation and performance. However, the volume of data on the Internet has reached a level that makes manual processing almost impossible, demanding new analytical approaches. Sentiment analysis is rapidly emerging as an automated process of examining semantic relationships and meaning in reviews. In this article, different sentiment analysis approaches applied in tourism are reviewed and assessed in terms of the datasets used and performances on key evaluation metrics. The article concludes by outlining future research avenues to further advance sentiment analysis in tourism as part of a broader Big Data approach.

Instafamous and social media influencer marketing

Abstract: The purpose of this paper is to test the effects of two types of celebrities (Instagram celebrity vs traditional celebrity) on source trustworthiness, brand attitude, envy and social presence. The proposed theoretical model consists of the celebrity type as the independent variable, social presence as the mediator and self-discrepancy as the moderator.,A randomized two-group comparison (Instagram celebrity vs traditional celebrity) between-subjects experiment (n=104) was conducted.,The results indicate that consumers exposed to Instagram celebrity’s brand posts perceive the source to be more trustworthy, show more positive attitude toward the endorsed brand, feel stronger social presence and feel more envious of the source than those consumers exposed to traditional celebrity’s brand posts. Structural equation modeling (Mplus 8.0) and bootstrap confidence intervals indicate that social presence mediates the causal effects of celebrity type on trustworthiness, brand attitude and envy. Multiple regression analyses reveal the moderating effects of appearance-related actual–ideal self-discrepancy.,Ultimately, managerial implications for social media marketing and Instagram influencer-based branding are provided. From the perspective of marketing planning, the findings speak to the power of influencer marketing as an effective branding strategy.,The paper discusses theoretical implications for the marketing literature on celebrity endorsements.

Trends in U.S. Adolescents’ media use, 1976–2016: The rise of digital media, the decline of TV, and the (near) demise of print.

Abstract: Studies have produced conflicting results about whether digital media (the Internet, texting, social media, and gaming) displace or complement use of older legacy media (print media such as books, magazines, and newspapers; TV; and movies). Here, we examine generational/time period trends in media use in nationally representative samples of 8th, 10th, and 12th graders in the United States, 1976–2016 (N 1,021,209; 51% female). Digital media use has increased considerably, with the average 12th grader in 2016 spending more than twice as much time online as in 2006, and with time online, texting, and on social media totaling to about 6 hr a day by 2016. Whereas only half of 12th graders visited social media sites almost every day in 2008, 82% did by 2016. At the same time, iGen adolescents in the 2010s spent significantly less time on print media, TV, or movies compared with adolescents in previous decades. The percentage of 12th graders who read a book or a magazine every day declined from 60% in the late 1970s to 16% by 2016, and 8th graders spent almost an hour less time watching TV in 2016 compared with the early 1990s. Trends were fairly uniform across gender, race/ethnicity, and socioeconomic status. The rapid adoption of digital media since the 2000s has displaced the consumption of legacy media.

Mobile Devices and Health.

Abstract: Mobile Devices and Health Mobile health involves sensors, mobile apps, social media, and location-tracking technology used in disease diagnosis, prevention, and management. This article provides an...

Reuters Institute digital news report 2019

Abstract: The eighth Digital News Report from the Reuters Institute for the Study of Journalism at the University of Oxford reveals that public concern about misinformation is making some people more careful about the brands they choose and the content they share online. The report, which is based on a YouGov survey conducted with 75,000 people in 38 markets, says that changing behaviour is most apparent with those that are younger and better educated, rather than older or less privileged groups. Across countries over a quarter (26%) say they are relying on more ‘reputable’ sources of news than this time last year – rising to 40% in the US. A further quarter (24%) said they had stopped using sources with a dubious reputation. The report also brings new comparative data on changing online business models, trust, misinformation, the rise of messaging apps and the impact of populism on media usage.

A survey of sentiment analysis in social media

Abstract: Sentiments or opinions from social media provide the most up-to-date and inclusive information, due to the proliferation of social media and the low barrier for posting the message. Despite the growing importance of sentiment analysis, this area lacks a concise and systematic arrangement of prior efforts. It is essential to: (1) analyze its progress over the years, (2) provide an overview of the main advances achieved so far, and (3) outline remaining limitations. Several essential aspects, therefore, are addressed within the scope of this survey. On the one hand, this paper focuses on presenting typical methods from three different perspectives (task-oriented, granularity-oriented, methodology-oriented) in the area of sentiment analysis. Specifically, a large quantity of techniques and methods are categorized and compared. On the other hand, different types of data and advanced tools for research are introduced, as well as their limitations. On the basis of these materials, the essential prospects lying ahead for sentiment analysis are identified and discussed.

Trends in the diffusion of misinformation on social media

Abstract: In recent years, there has been widespread concern that misinformation on social media is damaging societies and democratic institutions. In response, social media platforms have announced actions ...

Social media’s enduring effect on adolescent life satisfaction

Abstract: In this study, we used large-scale representative panel data to disentangle the between-person and within-person relations linking adolescent social media use and well-being. We found that social media use is not, in and of itself, a strong predictor of life satisfaction across the adolescent population. Instead, social media effects are nuanced, small at best, reciprocal over time, gender specific, and contingent on analytic methods.

The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan

Abstract: In today’s world, social media is playing an indispensable role on the learning behavior of university students to achieve sustainable education. The impact of social media on sustainable education is becoming an essential and impelling factor. The world has become a global village and technology use has made it a smaller world through social media and how it is changing instruction. This original study is amongst the few to perform a focalized investigation on revealing the relationship between positive and negative characteristics of social media and the learning attitude of university students for sustainable education. However, this study aims to examine the constructive and adverse factors that impact on students’ minds and how these helped students to share positive and negative aspects with others. It is increasingly noticeable that social networking sites and their applications present enormous benefits for as well as risks to university students and their implications on students’ psychological adjustment or learning behaviors are not well understood. This study adapted the cluster sampling method, and respondents participated from five selected regions. Researchers distributed 1013 questionnaires among the targeted sample of university students with an age range of 16 to 35 years, and they collected 831 complete/valid responses. This study applied the social gratification theory to examine students’ behavior practicing social media usage. This study specifically identified 18 adversarial and constructive factors of social media from the previous literature. The findings revealed that the usage of social media in Pakistan has a negative influence on a student’s behavior as compared to positive aspects. Results may not be generalized to the entire student community as findings are specific to the specific respondents only. This study presents a relationship between antithetical and creative characteristics of social media and exhibits avenues for future studies by facilitating a better understanding of web-based social network use.

Mapping and recontextualizing the evolution of the term Latinx: An environmental scanning in higher education

Abstract: The term Latinx emerged recently as a gender-neutral label for Latino/a and Latin@. The purpose of this paper is to examine ways in which Latinx is used within the higher education context, and to ...

Data-Driven Cybersecurity Incident Prediction: A Survey

Abstract: Driven by the increasing scale and high profile cybersecurity incidents related public data, recent years we have witnessed a paradigm shift in understanding and defending against the evolving cyber threats, from primarily reactive detection toward proactive prediction. Meanwhile, governments, businesses, and individual Internet users show the growing public appetite to improve cyber resilience that refers to their ability to prepare for, combat and recover from cyber threats and incidents. Undoubtedly, predicting cybersecurity incidents is deemed to have excellent potential for proactively advancing cyber resilience. Research communities and industries have begun proposing cybersecurity incident prediction schemes by utilizing different types of data sources, including organization’s reports and datasets, network data, synthetic data, data crawled from webpages, and data retrieved from social media. With a focus on the dataset, this survey paper investigates the emerging research by reviewing recent representative works appeared in the dominant period. We also extract and summarize the data-driven research methodology commonly adopted in this fast-growing area. In consonance with the phases of the methodology, each work that predicts cybersecurity incident is comprehensively studied. Challenges and future directions in this field are also discussed.

Behind the Screen: Content Moderation in the Shadows of Social Media

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What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence:

Abstract: The authors test five theoretically derived hypotheses about what drives video ad sharing across multiple social media platforms. Two independent field studies test these hypotheses using 11 emotio...

Social Bias Frames: Reasoning about Social and Power Implications of Language

Abstract: Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people's judgments about others. For example, given a statement that "we shouldn't lower our standards to hire more women," most listeners will infer the implicature intended by the speaker -- that "women (candidates) are less qualified." Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80% F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.

Comparison and Modelling of Country-level Microblog User and Activity in Cyber-physical-social Systems Using Weibo and Twitter Data

Abstract: As the rapid growth of social media technologies continues, Cyber-Physical-Social System (CPSS) has been a hot topic in many industrial applications. The use of “microblogging” services, such as Twitter, has rapidly become an influential way to share information. While recent studies have revealed that understanding and modelling microblog user behaviour with massive users’ data in social media are keen to success of many practical applications in CPSS, a key challenge in literatures is that diversity of geography and cultures in social media technologies strongly affect user behaviour and activity. The motivation of this article is to understand differences and similarities between microblogging users from different countries using social media technologies, and to attempt to design a Country-Level Micro-Blog User (CLMB) behaviour and activity model for supporting CPSS applications. We proposed a CLMB model for analysing microblogging user behaviour and their activity across different countries in the CPSS applications. The model has considered three important characteristics of user behaviour in microblogging data, including content of microblogging messages, user emotion index, and user relationship network. We evaluated CLBM model under the collected microblog dataset from 16 countries with the largest number of representative and active users in the world. Experimental results show that (1) for some countries with small population and strong cohesiveness, users pay more attention to social functionalities of microblogging service; (2) for some countries containing mostly large loose social groups, users use microblogging services as a news dissemination platform; (3) users in countries whose social network structure exhibits reciprocity rather than hierarchy will use more linguistic elements to express happiness in microblogging services.

Passive crowdsourcing of social media in environmental research: A systematic map

Abstract: The analysis of data from social media and social networking sites may be instrumental in achieving a better understanding of human-environment interactions and in shaping future conservation and environmental management. In this study, we systematically map the application of social media data in environmental research. The quantitative review of 169 studies reveals that most studies focus on the analysis of people’s behavior and perceptions of the environment, followed by environmental monitoring and applications in environmental planning and governance. The literature testifies to a very rapid growth in the field, with Twitter (52 studies) and Flickr (34 studies) being most frequently used as data sources. A growing number of studies combine data from multiple sites and jointly investigates multiple types of media. A broader, more qualitative review of the insights provided by the investigated studies suggests that while social media data offer unprecedented opportunities in terms of data volume, scale of analysis, and real-time monitoring, researchers are only starting to cope with the challenges of data’s heterogeneity and noise levels, potential biases, ethics of data acquisition and use, and uncertainty about future data availability. Critical areas for the development of the field include integration of different types of information in data mashups, development of quality assurance procedures and ethical codes, improved integration with existing methods, and assurance of long-term, free and easy-to-access provision of public social media data for future environmental researchers.

Online relationship marketing

Abstract: Online interactions have emerged as a dominant exchange mode for companies and customers. Cultivating online relationships—defined as relational exchanges that are mediated by Internet-based channels—presents firms with challenges and opportunities. In lockstep with exponential advancements in computing technology, a rich and ever-evolving toolbox is available to relationship marketers to manage customer relationships online, in settings including e-commerce, social media, online communities, mobile, big data, artificial intelligence, and augmented reality. To advance academic knowledge and guide managerial decision making, this study offers a comprehensive analysis of online relationship marketing in terms of its conceptual foundations, evolution in business practice, and empirical insights from academic research. The authors propose an evolving theory of online relationship marketing, characterizing online relationships as uniquely seamless, networked, omnichannel, personalized, and anthropomorphized. Based on these five essential features, six tenets and 11 corresponding propositions parsimoniously predict the performance effects of the manifold online relationship marketing strategies.