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This study aims to find how can fashion micro-influencers and their electronic word-of-mouth (eWOM) messages increase consumer engagement on social media, focusing on micro-influencers’ influence, typology, eWOM content and consumer engagement.

A total of 20,000 microblogs were collected from Irish fashion micro-influencers and analyzed through keyword classification and content analysis in NVivo. The determinants of eWOM persuasiveness for consumer engagement on social media were investigated based on Sussman and Siegal’s information adoption model.

The study finds that among the four types of micro-influencers, market mavens and their eWOM messages have the highest impact on consumer engagement on social media, and it presents a repetitive and persuasive eWOM model of market mavens to increase consumer participation. Also, the study discovers that micro-influencers’ occasion-related microblogs have an increasing impact on consumer interactions whereas microblogs with brands have a decreasing engagement with consumers on social media.

This study advances prior studies on the relationship between influencers’ eWOM messages and consumer participation on social media by the development of a persuasive eWOM model of micro-influencers to increase consumer engagement and fill in the lack of relevant literature. Also, findings provide actionable insights for marketing communication practitioners to persuade consumers to participate in eWOM communications and establish strong consumer-brand relationships on social media.

By drawing on the research paradigm of collective action that occurs in physical space, the present study aims to explore the antecedent predictors of network social mobilization – feeling of injustice – and discuss the emotional mechanism of this prediction: mediating effect of anger and resentment.

Micro-blog postings about network social mobilization were collected to develop the dictionary of codes of fairness, anger and resentment. Then, according to the dictionary, postings on Sina Weibo were coded and analyzed.

The feeling of injustice predicted network social mobilization directly. The predictive value was 27% and 33%, respectively during two analyses. The feeling of injustice also predicted social mobilization indirectly via anger and resentment. In other words, anger and resentment account for the active mechanism in which the feeling of injustice predicts network social mobilization. Mediating effect value was 29.63% and 33.33% respectively.

This study is our first exploration to use python language to collect data from human natural language pointing on micro-blog, a large number of comments of netizen about certain topic were crawled, but a small portion of the comments could be coded into analyzable data, which results in a doubt of the reliability of the study. Therefore, we should put the established model under further testing.

In the cyberspace, this study confirms the mechanism of network social mobilization, expands and enriches the research on social mobilization and deepens the understanding of social mobilization.

This study provides an empirical evidence to understand the network social mobilization, and it gives us the clue to control the process of network social mobilization.

This study uses the Python language to write Web crawlers to obtain microblog data and analyze the microblog content for word segmentation and matching thesaurus. It has certain innovation.

The authors examine the contemporaneous and causal association between tweet features (bullishness, message volume and investor agreement) and market features (stock returns, trading volume and volatility) using 140 South African companies and a dataset of firm-level Twitter messages extracted from Bloomberg for the period 1 January 2015 to 31 March 2020.

Panel regressions with ticker fixed-effects are used to examine the contemporaneous link between tweet features and market features. To examine the link between the magnitude of tweet features and stock market features, the study uses quantile regression.

No monotonic relationship is found between the magnitude of tweet features and the magnitude of market features. The authors find no evidence that past values of tweet features can predict forthcoming stock returns using daily data while weekly and monthly data shows that past values of tweet features contain useful information that can predict the future values of stock returns.

The study is among the earlier to examine the association between textual sentiment from social media and market features in a South African context. The exploration of the relationship across the distribution of the stock market features gives new insights away from the traditional approaches which investigate the relationship at the mean.

This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch.

The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector).

This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness.

This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.

This paper investigates the usage and trust of Chinese social media in the travel planning process (pre-trip, during-trip and post-trip) of Chinese tourists.

Through a combination of structured online survey (n = 406) and follow-up interviews, the research identifies the diversification of the demand-and-supply patterns of social media users in China, as well as the allocation of functions of social media as tools before, during and after travel.

Social media users are diverse in terms of their adoption of social media, use behaviour and scope; the levels of trust and influence; and their ultimate travel decisions and actions. Correlations between the level of trust, influence of social media and the intended changes in travel decisions are observed. Destination marketers and tourism industries should observe and adapt to the needs of social media users and potential tourist markets by understanding more about user segmentation between platforms or apps and conducting marketing campaigns on social media platforms to attract a higher number of visitors.

This paper demonstrated the case of social media usage in mainland China, which has been regarded as one of the fastest growing and influential tourist-generating markets and social media expansions in the world. This study further addressed the knowledge gap by correlating social media usage and travel planning process of Chinese tourists. The research findings suggested diversification of the demand-and-supply pattern of social media users in China, as well as the use of social media as tools before, during and after travel. Users were diversified in terms of their adoption of social media, use behaviour, scope, the levels of trust, influence and the ultimate travel decisions.

Destination marketing organizations should note that some overseas social media platforms that are not accessible in China like TripAdvisor, Yelp, Facebook and Instagram are still valued by some Chinese tourists, especially during-trip period in journeys to Western countries. Some tactics for specific user segments should be carefully observed. When promoting specific tourism products to Chinese tourists, it is necessary to understand the user segmentation between platforms or apps.

Social media is a powerful tool for tourism development and sustainability in creating smart tourists and destinations worldwide. In China, the use of social media has stimulated the development of both information and communication technology and tourism.

This chapter analyses the Norwegian Twitter-sphere during and in the aftermath of the terrorist attack in Norway on 22 July 2011. Based on a collection of 2.2 million tweets representing the Twitter-sphere during the period 20 July–28 August 2011, the chapter seeks answers to how the micro-blogging services aided in creating situation awareness (SA) related to the emergency event, what role hashtags played in that process and who the dominant crisis communicators were. The chapter is framed by theories and previous research on SA and social media use in the context of emergency events. The findings reveal that Twitter was important in establishing SA both during and in the aftermath of the terrorist attack, that hashtags were of limited value in this process during the critical phase, and that unexpected actors became key communicators.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

Twitter is the most widely used platform with an open network; hence, tourists often resort to Twitter to share their travel experiences, satisfaction/dissatisfaction and other opinions. This study is divided into two sections, first to provide a framework for understanding public sentiments through Twitter for tourism insights, second to provide real-time insights of three Indian heritage sites i.e., the Taj Mahal, Red Fort and Golden Temple by extracting 5,000 tweets each (n = 15,000) using Twitter API. Results are interpreted using NRC emotion lexicon and data visualisation using R.

This study attempts to understand the public sentiment on three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple using a step-by-step approach, hence proposing a framework using Twitter analytics. Extensive use of various packages of R programming from the libraries has been done for various purposes such as extraction, processing and analysing the data from Twitter. A total of 15,000 tweets from January 2015 to January 2021 were collected of the three sites using different key words. An exploratory design and data visualisation technique has been used to interpret results.

After data processing, 12,409 sentiments are extracted. Amongst the three tourists' spots, the greatest number of positive sentiments is for the Taj Mahal and Golden temple with approximately 25% each. While the most negative sentiment can be seen for the Red Fort (17%). Amongst the positive emotions, the maximum joy sentiment (12%) can be seen in the Golden Temple and trust (21%) in the Red Fort. In terms of negative emotions, fear (13%) can be seen in the Red fort. Overall, India's heritage sites have a positive sentiment (20%), which surpasses the negative sentiment (13%). And can be said that the overall polarity is towards positive.

This study provides a framework on how to use Twitter for tourism insights through text mining public sentiments and provides real- time insights from famous Indian heritage sites.