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Now more than ever, businesses need to understand what the media is saying about them. The authors describe three types of media analysis: salience and sentiment analysis; theme and contradiction analysis; and problem and solution analysis, the first two of which are routinely commissioned by many companies. Using four case studies the authors describe how problem and solution analysis can be used to save costs and increase revenues.

Four case studies are used to illustrate the financial value that problem and solution media analysis can play in understanding and solving a range of business problems.

The authors show how the analysis of media commentary helped a public company to identify its most influential investment commentators; helped an appliance manufacturer to change its sales force compensation scheme; helped a financial services company to position its IPO; and helped an internet‐based share trading company to understand some conflicting research results. The financial value of these outcomes often far exceeded the price paid.

The authors compare and contrast three styles of media analytics. The review suggests that the problem and solution analysis technique is novel and financially valuable in situations where media coverage creates problems.

The study aims to examine the validity of the gender affinity effect on social media throughout election campaigns.

This paper examines the role of gender in political discourse, using citizens' conversations on Facebook in the days leading up to Israel's 2021 elections as its case study. The analysis measured the engagement generated by male and female politicians in citizens' publicly open Facebook discussions (N = 1875) using a trend-tracking software. The analysis uses t-tests to examine differences in engagement between conversations about male versus female politicians and between posts written by male versus female authors. In addition, a two-way ANOVA analysis was conducted in an attempt to understand the shared impact of both the politicians' gender and posts authors' gender on the posts' engagement.

The study reveals that although more posts discuss male politicians, posts dealing with female politicians expressed significantly more support towards those politicians. The analysis also highlights that women tend to write more supportive posts and that most of their posts deal with female politicians. Furthermore, interaction effect analysis revealed that women's posts about female politicians generate more engagement in terms of likes, comments and number of participants than posts written by women that deal with male politicians.

The findings should encourage women politicians to run their campaigns via social media.

The study presents the first social media analysis for gender affinity effect and highlights the importance of the effect in online political communication studies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2022-0199

This study employs bibliometric analysis to map the research landscape of social media trending topics during the COVID-19 pandemic. The authors aim to offer a comprehensive review of the predominant research organisations and countries, key themes and favoured research methodologies pertinent to this subject.

The authors extracted data on social media trending topics from the Web of Science Core Collection database, spanning from 2009 to 2022. A total of 1,504 publications were subjected to bibliometric analysis, utilising the VOSviewer tool. The study analytical process encompassed co-occurrence, co-authorship, citation analysis, field mapping, bibliographic coupling and co-citation analysis.

Interest in social media research, particularly on trending topics during the COVID-19 pandemic, remains high despite signs of the pandemic stabilising globally. The study predominantly addresses misinformation and public health communication, with notable focus on interactions between governments and the public. Recent studies have concentrated on analysing Twitter user data through text mining, sentiment analysis and topic modelling. The authors also identify key leading organisations, countries and journals that are central to this research area.

Diverging from the narrow focus of previous literature reviews on social media, which are often confined to particular fields or sectors, this study offers a broad view of social media's role, emphasising trending topics. The authors demonstrate a significant link between social media trends and public events, such as the COVID-19 pandemic. The paper discusses research priorities that emerged during the pandemic and outlines potential methodologies for future studies, advocating for a greater emphasis on qualitative approaches.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2023-0194.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

The significance of social media in our lives is manifold. The tourism sector closely interacts with existing and potential tourists through social media, and therefore, social media analytics (SMA) play a critical role in the uplift of the sector. Hence, this review focus on the role of SMA in tourism as discussed in different studies over a period of time. The purpose of this paper to present the state of the art on social media analytics in tourism.

The review focuses on identifying different SMA techniques to explore the trends and approaches adopted in the tourism sector. The review is based on 83 papers and discuss the studies related to different social media platforms, the travelers' reactions to a particular place and how the tourism experience is enriched by the way of SMA.

Findings indicate different sentiments associated with tourism and provides a review of tourists’ use of social media for choosing a travel destination. The various analytical approaches, areas such as social network analysis, content analysis, sentiment analysis and trend analysis were found most prevalent. The theoretical and practical implications of SMA are discussed. The paper made an effort to bridge the gap between different studies in the field of tourism and SMA.

SMA facilitate both tourists and tourism companies to understand the trends, sentiments and desires of tourists. The use of SMA offers value to companies for designing quick and adequate services to tourists.

SWOT (strengths, weaknesses, opportunities, threats) analysis is a powerful approach for evaluating the strengths and weaknesses of an organization with an internal perspective. The approach also takes into account the opportunities and the threats from an external point of view. These features make SWOT a commonly used approach in strategic management. The purpose of this paper is to propose an integrated SWOT analysis with multiple preference relations technique, to show the application of the proposed methodology, to prioritize the strategic factors and to present alternative strategies for ABC, a case company, which is targeting to use social media more effectively.

In this study, expert opinions are used to identify SWOT factors of ABC on social media. The obtained findings are evaluated and each factor is prioritized by means of the multiple preference relations technique.

The proposed evaluation model has four main groups, namely, strengths, weaknesses, opportunities, threats, under which 17 factors are identified. As a result of the evaluations, “O2: Opportunity to contact a large number of users simultaneously at affordable cost” has the highest importance level among other factors. Alternative strategies are developed based on the obtained results.

Decision-makers who have different backgrounds or ideas can state their preferences in different formats. Multiple preference relations technique is used to combine different assessments. SWOT analysis with multiple preference relations technique with a group decision-making perspective is proposed. This is the first time the method is used in the social media-related literature. With this study, the most appropriate social media strategic factors are selected for ABC and alternative strategies are determined based on the results.

There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies.

Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform.

Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided.

The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy.

Twitter analysis of food-based companies has been performed.

India has the biggest number of active users on social media platforms, particularly Twitter. The purpose of this paper is to examine public sentiment on COVID-19 vaccines and COVID Appropriate Behaviour (CAB) by text mining (topic modeling) and network analysis supported by thematic modeling.

A sample dataset of 115,000 tweets from the Twitter platform was used to examine the perception of the COVID-19 vaccination and CAB from January 2021 to August 2021. The research applied a machine-learning algorithm and network analysis to extract hidden and latent patterns in unstructured data to identify the most prevalent themes. The COVID-19 Vaccine Hesitancy Amplification Model was formulated, which included five key topics based on sample big data from social media.

The identified themes are Social Media Adaptivity, Lack of Knowledge Providing Mechanism, Perception of Vaccine Safety Measures, Health Care Infrastructure Capabilities and Fear of Coronavirus (Coronaphobia). The study implication assists communication strategists and stakeholders design effective communication strategies using digital platforms. The study reveals CAB themes as with Mask Wearing Issues and Employment Issues as relevant themes discussed on digital channels.

The themes extracted in the present study provide a roadmap for policy-makers and communication experts to utilize social media platforms for communicating and understanding the perception of preventive measures of vaccination and CAB. As evidenced by the increased engagement on social media platforms during the COVID-19-induced lockdown, digital platforms are indeed valuable from the communication perspective to be proactive in the event of a similar situation. Moreover, significant themes, including social media adaptivity, absence of knowledge-providing mechanism and perception of safety measures of the vaccine, are the critical parameters leading to an amplified effect on vaccine hesitancy.

The COVID-19 Vaccine Hesitancy Amplification Themes (CVHAT) equips stakeholders and government strategists with a preconfigured paradigm to tackle dedicated communication campaigns and assess digital community behavior during health emergencies COVID-19.

The increased acceptance of vaccines and the following of CAB decrease the advocacy of mutation of the virus and promote the healthy being of the people. As CAB has been mentioned as a preventive strategy against the COVID-19 pandemic, the research preposition promotes communication intervention which helps to mitigate future such pandemics. As developing, economies require effective communication strategies for vaccine acceptance and CAB, this study contributes to filling the gap using a digital environment.

Chan et al. (2020) recommended using social media platforms for public knowledge dissemination. The study observed that the value of a communication strategy is increased when communication happens using highly trusted and accessible channels such as Twitter and Facebook. With the preceding context, the present study is a novel approach to contribute toward digital communication strategies related to vaccination and CAB.

This study aims to explain the impact of the Beijing Olympic Games 2008 on China's image in the international TV media. It applies agenda-setting theory to analyse foreign TV coverage of the Olympics in nine countries. Using Rivenburgh's national image richness construct, it attempts to make sense of the coverage before and after Beijing 2008, particularly its impact on the image of the host country. The study concludes that the breadth and attribution of China's image remained relatively stable, that these factors did not improve China's national image directly but that indirectly they raised awareness of China in the international media and framed the host country's image more clearly.

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.