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As human beings step into the age of information network, big data technology is constantly improving the intelligence level of various agents such as individuals and enterprises. The crowd decision-making of the intellectual community plays an important role in the active participation of many individuals and schools in giving their wisdom, effectively solve the problems of negative internet communication, single publicity media and unprofessional promotion team in WeChat public account.

This paper aims to optimize the content and improve the effectiveness of network ideological and political education in universities. This study analyzes five highly popular WeChat public accounts at the Central University of Finance and Economics in 2019. It obtains the popularity index of tweets using the WeChat communication index algorithm and finds that the important factors that influence tweet popularity are release time and content value.

To improve the public account tweets, this study highlights the connection between the tweets’ value and students’ emotional needs, which enhances the value of tweet content in students’ life and provides more original and distinctive content.

This study found that the content and interest of college students are tweet time, tweet value and tweet content. Therefore, the public account of college ideological and political education should be improved from the following three aspects: realizing the connection between the value of tweet content and students’ emotional needs; enhancing the value of tweet content in students’ life and learning; and insisting on the original and distinctive original intention of tweet content.

This paper aims to create a better understanding of the challenges posed by work from home (WFH) during the ongoing COVID-19 pandemic, to investigate the public sentiment toward this transition, and to develop a conceptual model incorporating the relationships among the factors that influence the effectiveness of WFH.

This paper uses netnography method to collect data from the Twitter platform and uses Python programming language, Natural Language Processing techniques and IBM SPSS 26 to conduct sentiment analysis and directed content analysis on the data. The findings are combined with an extensive review of the remote work literature to develop a conceptual model.

Results show the majority of tweets about WFH during the pandemic are positive and objective with technology and cyber security as the most repeated topics in the tweets. New challenges to WFH during pandemic include future uncertainty, health concerns, home workspaces, self-isolation, lack of recreational activities and support mechanisms. In addition, exhaustion and technostress mediate the relationship between the antecedents and outcomes of WFH during the ongoing COVID-19 pandemic. Finally, the fear of pandemic and coping strategies moderates these relationships.

This paper is one of the first efforts to comprehensively investigate the challenges of WFH during a crisis and to extend the remote work literature by developing a conceptual model incorporating the moderating effects of fear of pandemic and coping strategies. Moreover, it is the first paper to investigate the tweeting behavior of different user types on Twitter who shared posts about WFH during the ongoing pandemic.

This chapter provides an overview of the specific legal, ethical, and privacy issues that can arise when conducting research using Twitter data. Existing literature is reviewed to inform those who may be undertaking social media research. We also present a number of industry and academic case studies in order to highlight the challenges that may arise in research projects using social media data. Finally, the chapter provides an overview of the process that was followed to gain ethics approval for a Ph.D. project using Twitter as a primary source of data. By outlining a number of Twitter-specific research case studies, the chapter will be a valuable resource to those considering the ethical implications of their own research projects utilizing social media data. Moreover, the chapter outlines existing work looking at the ethical practicalities of social media data and relates their applicability to researching Twitter.

This chapter examines how established media – that is, print, TV and radio sources which pre-existed the popularisation of social media – use social media to disseminate content. Specifically it examines the manner in which three UK media sources – BBC News, The Guardian and the Daily Mail – used Twitter during the 2014–2015 Ebola crisis. It asks five key questions concerning: the balance between factual reporting and opinion or comment; the degree to which it shifted attention to specific events within the context of the outbreak; whether the dialogical potential of social media was exploited; the degree to which social media acted as a signpost to more detailed information elsewhere, or existed as independent content; and the degree of media reflexivity. It concludes that established media used this new technology within their existing paradigms for reporting rather than exploiting some of its more innovative characteristics.

Social media platform, like Twitter, has increasingly become the mode of reporting civic issues owing to their vast and fast reachability. A tremendous amount of information on urban issues is shared every moment out of which some tweets may need immediate attention to save lives or avoid future disasters. Existing approaches are only limited to the identification of complaint tweets; however, its prioritization based on urgency is still unexplored. This study aims to decide the ranking of complaints based on its criticality derived using multiple parameters, like type of complaint, season, day or night, gender, holiday or working day, etc.

The approach proposes an ensemble of multi-class classification (MCC) and “two-level” multi-criteria decision-making (MCDM) algorithms, like AHP and TOPSIS, to evaluate the accurate ranking score of the tweet based on the severity of the issue. Initially, the MCC is applied to tweets to categorize the tweets into three categories, i.e. moderate, urgent and immediate. Further, the first level of MCDM algorithm decides the ranking within each complaint type, and the second level evaluates the ranking across all types. Integration of MCC and MCDM methods further helps to increase the accuracy of the result.

The paper discusses various parameters and investigates how their combination plays a significant role in deciding the priority of complaints. It successfully demonstrates that MCDM techniques are helpful in generating the ranking score of tweets based on various criteria.

This paper fulfills an identified need to prioritize the complaint tweet which helps the local government to take time-bound actions and save a life.

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to estimate the political mood prevailing among the masses? Can they also be used to reliably predict the election outcome? To answer these in the Indian context, the 2014 general election was chosen.

Tweets posted on the leading parties during the voting and crucial campaign periods were mined and manual sentiment analysis was performed on them.

A strong and positive correlation was observed between the political sentiments expressed on Twitter and election results. Further, the Time Periods during which the tweets were mined were found to have a moderating effect on this relationship.

This study showed that the month preceding the voting period was the best to predict the vote share with Twitter data – with 83.9% accuracy.

Twitter has become an important public communication tool in India, and as the study results reinstate, it is an ideal research tool to gauge public opinion.

Social media platforms are increasingly receiving attention as legitimate locations for civil society discourse and social movement mobilization. Initial work by Lovejoy and Saxton suggests NGOs use digital platforms such as Twitter to engage their constituencies through information dissemination, community building, and mobilization to action. Here, we explore the applicability of Lovejoy and Saxton’s communicative functions framework to resistance movement behavior by exploring two examples of digital engagement in political conflict. Through content analysis of tweets using hashtag indicators #BDS and #ICC4Israel collected during the spring of 2015, we affirm Lovejoy and Saxton’s findings that information dissemination is the most prevalent communication function for grassroots and institutionally grounded movements. Further, we find that informational tweets in our sample often provide information about grievances, and therefore propose an expansion of the framework to accommodate tweets that may be more common in resistance movements than in NGO communication. In addition to general findings about the communicative functions framework, the content analysis yielded several findings specific to the resistance movements studied. Notably, we find that #BDS and #ICC4Israel tweets are overwhelmingly nonviolent, and that sentiment is generally favorable across both hashtags, with the exception of tweets focusing on academic boycott, which were more ambiguous.

Industry platforms can alter relations among exchange partners in such a way that the industry structure is changed. The focus of much industry platform research has been on how platform creation and leadership offers advantages to the most central firms, but platforms can also be advantageous for small specialist firms that compete with the most central firms. We examine book publishing as an example of an industry in which the central players – large publishing firms – are losing power to self-publishing authors because the distributor Amazon has a powerful platform for customers to communicate independently, and the non-publishing platform Twitter also serves as a medium for readers to discuss and review books. Our empirical analysis is based on downloaded sales statistics for Amazon Ebooks, matched with Amazon reviews of the same books and tweets that refer to the book or the author. We analyze how Ebook sales are a function of publisher, Amazon reviews, and tweets, and we are able to assess the importance of each factor in the sale of book titles. The main finding is that Amazon reviews are powerful drivers of book sales, and have greater effect on the sales of books that are not backed by publishers. Twitter also affects book sales, but less strongly than Amazon reviews.