Lies, brands and social media

2019 ◽  
Vol 22 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Tracy Tuten ◽  
Victor Perotti
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Coverage ◽  
Future Research ◽  
False Information ◽  
Content Type ◽  
News Organizations ◽  
Media Sentiment ◽  
The Impact ◽  
False Claim

Purpose The purpose of this study is to illustrate the influence of media coverage and sentiment about brands on user-generated content amplification and opinions expressed in social media. Design/methodology/approach This study used a mixed-method approach, using a brand situation as a case example, including sentiment analysis of social media conversations and sentiment analysis of media coverage. This study tracks the diffusion of a false claim about the brand via online media coverage, subsequent spreading of the false claim via social media and the resulting impact on sentiment toward the brand. Findings The findings illustrate the influence of digital mass communication sources on the subsequent spread of information about a brand via social media channels and the impact of the social spread of false claims on brand sentiment. This study illustrates the value of social media listening and sentiment analysis for brands as an ongoing business practice. Research limitations/implications While it has long been known that media coverage is in part subsequently diffused through individual sharing, this study reveals the potential for media sentiment to influence sentiment toward a brand. It also illustrates the potential harm brands face when false information is spread via media coverage and subsequently through social media posts and conversations. How brands can most effectively correct false brand beliefs and recover from negative sentiment related to false claims is an area for future research. Practical implications This study suggests that brands are wise to use sentiment analysis as part of their evaluation of earned media coverage from news organizations and to use social listening as an alert system and sentiment analysis to assess impact on attitudes toward the brand. These steps should become part of a brand’s social media management process. Social implications Media are presumed to be impartial reporters of news and information. However, this study illustrated that the sentiment expressed in media coverage about a brand can be measured and diffused beyond the publications’ initial reach via social media. Advertising positioned as news must be labeled as “advertorial” to ensure that those exposed to the message understand that the message is not impartial. News organizations may inadvertently publish false claims and relay information with sentiment that is then carried via social media along with the information itself. Negative information about a brand may be more sensational and, thus, prone to social sharing, no matter how well the findings are researched or sourced. Originality/value The value of the study is its illustration of how false information and media sentiment spread via social media can ultimately affect consumer sentiment and attitude toward the brand. This study also explains the research process for social scraping and sentiment analysis.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

The impact of involvement on engagement with brand posts

2019 ◽  
Vol 13 (3) ◽  
pp. 277-301
Author(s):  
Ana Margarida Barreto ◽  
Diogo Ramalho
Keyword(s):  
Social Media ◽  
Consumer Involvement ◽  
Future Research ◽  
High Involvement ◽  
Content Type ◽  
Level Of Involvement ◽  
Goods And Services ◽  
Business To Consumer ◽  
Post Hoc ◽  
The Impact

Purpose This paper aims to look at the effects of different levels of involvement (high and low) on social media (Facebook) users' engagement (likes, shares and comments) with different types and formats of brand content. Design/methodology/approach The authors analyzed user reactions to 1,156 Facebook posts from eight business-to-consumer brands (goods and services). Based on a post hoc test, four product/services were identified as belonging to the group of high-involvement and the other four as low involvement. Findings The data suggest that, when involvement is low, users in general engage more with brand posts regardless their format (text, image and post) or type (hedonic and informative), or even the interaction of both. Moreover, low involvement leads users prefer to comment on brand content, whereas higher involvement is associated with to sharing it. Exceptions were observed for images (both hedonic and informative) and for hedonic image and video in both low and high involvement users. Research limitations/implications The goal was not to measure users’ attention to each type of post. Moreover, the authors did not have access to information regarding which devices were used to access the online content and whether that aspect might have an impact on users’ reactions. Neither do they claim that engagement necessarily reflects positive reactions, as any content analysis of users’ reactions was beyond the scope of this project. Practical implications These findings are expected to help brand managers and social media strategists to better select content based on their marketing goals, as well as to provide a potential explanation for the success of campaigns. Originality/value As far as we are aware, no previous study has attempted to observe the mediated effect of consumer involvement on brand posts considering their type and format. We also believe that this is the first observation of how behavior differentiates according to the target audience’s level of involvement. This paper also proposes a convenient framework for categorizing social network sites content. Suggestions for future research are made at the end.

Exploring the influence of multimodal social media data on stock performance: an empirical perspective and analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hui Yuan ◽  
Yuanyuan Tang ◽  
Wei Xu ◽  
Raymond Yiu Keung Lau
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Textual Information ◽  
Stock Performance ◽  
Vector Autoregressive Models ◽  
Social Media Data ◽  
Content Type ◽  
Vector Autoregressive ◽  
Media Sentiment ◽  
Media Data

PurposeDespite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.Design/methodology/approachThis research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.FindingsThe empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.Originality/valueTo the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

Wearable IoT intelligent recommender framework for a smarter healthcare approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Iot Devices ◽  
The Right ◽  
Media Sentiment

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Understanding the selfie phenomenon: current insights and future research directions

2016 ◽  
Vol 50 (9/10) ◽  
pp. 1773-1788 ◽  
Author(s):  
Weng Marc Lim
Keyword(s):  
Social Media ◽  
Future Research ◽  
Research Directions ◽  
Content Type ◽  
Marketing Performance ◽  
The Social ◽  
Future Research Directions ◽  
Marketing Tool ◽  
The Impact

Purpose This paper aims to define the conceptual boundary of the selfie and to discuss the role of the selfie in the social media marketplace. Design/methodology/approach This paper extensively reviews and draws themes from the extant literature on consumer identities in the social media marketplace to explain the selfie phenomenon and to identify potentially fruitful directions for further research. Findings Current insights into the selfie phenomenon can be understood from socio-historical, technological, social media, marketing and ethical perspectives. Research limitations/implications Despite the limitations of a general review (e.g. absence of empirical data and analysis), this paper identifies multiple avenues to extend existing lines of inquiry on the selfie phenomenon. Thus, this paper should encourage further research on the topic in the academic and scientific community. Practical implications The selfie can be used as a marketing tool to improve marketing performance and accomplish marketing-related goals. Originality/value This paper sheds light on how marketing academics and practitioners can better understand the impact of the selfie in the social media marketplace.

Media attention and corporate disaster relief: evidence from China

2017 ◽  
Vol 26 (1) ◽  
pp. 2-12 ◽  
Author(s):  
Zhe Ouyang ◽  
Jiuchang Wei ◽  
Yu Xiao ◽  
Fei Wang
Keyword(s):  
Media Coverage ◽  
Disaster Relief ◽  
Future Research ◽  
Yushu Earthquake ◽  
Content Type ◽  
Media Attention ◽  
The Media ◽  
The Impact ◽  
Chinese Search Engine

Purpose The purpose of this paper is to examine the impact of media attention on corporate disaster relief. Design/methodology/approach The authors used a matched sample research design, which is considered more appropriate than a random sample design for studying events that have low-occurrence rates in general. For each donor firm in the Yushu earthquake, the authors matched the firm with a non-donor firm in the same industry and with a firm size of within ±30 percent of the total assets in the year prior to the year of the occurrence of the Yushu earthquake. Then, using the Baidu engine, which is the most popular Chinese search engine, the authors captured the online media attention to the donor firms and their disaster relief. Findings The authors found that media attention drove corporate disaster relief. Research limitations/implications Although the authors highlighted the role of the media as an important stakeholder in influencing corporate disaster relief, the authors did not fully explore the media’s influence. Future research should delve more deeply into the impact of the tenor of media coverage on corporate disaster relief. Originality/value This study reveals that the media, a particularly powerful stakeholder, can be a corporate disaster relief driver in China.

scholarly journals Accounting, accountability, social media and big data: revolution or hype?

2017 ◽  
Vol 30 (4) ◽  
pp. 762-776 ◽  
Author(s):  
Michela Arnaboldi ◽  
Cristiano Busco ◽  
Suresh Cuganesan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Future Research ◽  
Data Governance ◽  
Content Type ◽  
Knowledge Based ◽  
Decision Making Processes ◽  
Key Aspects ◽  
The Impact

Purpose The purpose of this paper is to outline an agenda for researching the relationship between technology-enabled networks – such as social media and big data – and the accounting function. In doing so, it links the contents of an unfolding area research with the papers published in this special issue of Accounting, Auditing and Accountability Journal. Design/methodology/approach The paper surveys the existing literature, which is still in its infancy, and proposes ways in which to frame early and future research. The intention is not to offer a comprehensive review, but to stimulate and conversation. Findings The authors review several existing studies exploring technology-enabled networks and highlight some of the key aspects featuring social media and big data, before offering a classification of existing research efforts, as well as opportunities for future research. Three areas of investigation are identified: new performance indicators based on social media and big data; governance of social media and big data information resources; and, finally, social media and big data’s alteration of information and decision-making processes. Originality/value The authors are currently experiencing a technological revolution that will fundamentally change the way in which organisations, as well as individuals, operate. It is claimed that many knowledge-based jobs are being automated, as well as others transformed with, for example, data scientists ready to replace even the most qualified accountants. But, of course, similar claims have been made before and therefore, as academics, the authors are called upon to explore the impact of these technology-enabled networks further. This paper contributes by starting a debate and speculating on the possible research agendas ahead.

Measuring the impact of COVID-19 papers on the social web: an altmetric study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Metwaly Ali Mohamed Edakar ◽  
Ahmed Maher Khafaga Shehata
Keyword(s):  
Social Media ◽  
Social Impact ◽  
Service Providers ◽  
Research Performance ◽  
Future Research ◽  
Content Type ◽  
The Social ◽  
Social Media Platforms ◽  
Number Of Citations ◽  
The Impact

Purpose The rapid spread and severity of the coronavirus (COVID-19) virus have prompted a spate of scholarly research that deals with the pandemic. The purpose of this study is to measure and assess the coverage of COVID-19 research on social media and the engagement of readers with COVID-19 research on social media outlets. Design/methodology/approach An altmetric analysis was carried out in three phases. The first focused on retrieving all papers related to COVID-19. Phase two of the research aimed to measure the presence of the retrieved papers on social media using altmetric application programming interface (API). The third phase aimed to measure Mendeley readership categories using Mendeley API to extract data of readership from Mendeley for each paper. Findings The study suggests that while social media platforms do not give accurate measures of the impact as given by citations, they can be used to portray the social impact of the scholarly outputs and indicate the effectiveness of COVID-19 research. The results confirm a positive correlation between the number of citations to articles in databases such as Scopus and the number of views on social media sites such as Mendeley and Twitter. The results of the current study indicated that social media could serve as an indicator of the number of citations of scientific articles. Research limitations/implications This study’s limitation is that the studied articles’ altmetrics performance was examined using only one of the altmetrics data service providers (altmetrics database). Hence, future research should explore altmetrics on the topic using more than one platform. Another limitation of the current research is that it did not explore the academic social media role in spreading fake information as the scope was limited to scholarly outputs on social media. The practical contribution of the current research is that it informs scholars about the impact of social media platforms on the spread and visibility of COVID-19 research. Also, it can help researchers better understand the importance of published COVID-19 research using social media. Originality/value This paper provides insight into the impact of COVID-19 research on social media. The paper helps to provide an understanding of how people engage with health research using altmetrics scores, which can be used as indicators of research performance.

Social media platforms and sleep problems: a systematic literature review, synthesis and framework for future research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Puneet Kaur ◽  
Amandeep Dhir ◽  
Amal Khalifa Alkhalifa ◽  
Anushree Tandon
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Media Use ◽  
Future Research ◽  
Social Media Use ◽  
Content Type ◽  
Research Profile ◽  
Social Media Platforms ◽  
The Impact

PurposeThis study is a systematic literature review (SLR) on prior research examining the impact of the nocturnal use of social media platforms on a user's sleep, its dimensions and its perceptually allied problems. This SLR aims to curate, assimilate and critically examine the empirical research in this domain.Design/methodology/approachForty-five relevant studies identified from the Scopus and Web of Science (WoS) databases were analyzed to develop a comprehensive research profile, identify gaps in the current knowledge and delineate emergent research topics.FindingsPrior research has narrowly focused on investigating the associations between specific aspects of social media use behavior and sleep dimensions. The findings suggest that previous studies are limited by research design and sampling issues. We highlight the imperative need to expand current research boundaries through a comprehensive framework that elucidates potential issues to be addressed in future research.Originality/valueThe findings have significant implications for clinicians, family members and educators concerning promoting appropriate social media use, especially during sleep latency.

Location impact on source and linguistic features for information credibility of social media

2019 ◽  
Vol 43 (1) ◽  
pp. 89-112 ◽  
Author(s):  
Suliman Aladhadh ◽  
Xiuzhen Zhang ◽  
Mark Sanderson
Keyword(s):  
Social Media ◽  
Information Source ◽  
Physical Distance ◽  
Future Research ◽  
Semantic Features ◽  
Content Type ◽  
Other Information ◽  
Social Media Platforms ◽  
The Impact ◽  
The Relationship

PurposeSocial media platforms provide a source of information about events. However, this information may not be credible, and the distance between an information source and the event may impact on that credibility. Therefore, the purpose of this paper is to address an understanding of the relationship between sources, physical distance from that event and the impact on credibility in social media.Design/methodology/approachIn this paper, the authors focus on the impact of location on the distribution of content sources (informativeness and source) for different events, and identify the semantic features of the sources and the content of different credibility levels.FindingsThe study found that source location impacts on the number of sources across different events. Location also impacts on the proportion of semantic features in social media content.Research limitations/implicationsThis study illustrated the influence of location on credibility in social media. The study provided an overview of the relationship between content types including semantic features, the source and event locations. However, the authors will include the findings of this study to build the credibility model in the future research.Practical implicationsThe results of this study provide a new understanding of reasons behind the overestimation problem in current credibility models when applied to different domains: such models need to be trained on data from the same place of event, as that can make the model more stable.Originality/valueThis study investigates several events – including crisis, politics and entertainment – with steady methodology. This gives new insights about the distribution of sources, credibility and other information types within and outside the country of an event. Also, this study used the power of location to find alternative approaches to assess credibility in social media.

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