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Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.

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.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

In this chapter we discuss the implications social media have for the self-representation and identity formulation of professionals within organizations. Under the assumption that new, technology-mediated networking possibilities call for a reformulation of the boundaries between the professional and the private, we propose several avenues of investigation. The concept of “online personae” is also introduced in order to describe how managers may strive for equilibrium while balancing on and offline identities with impression management efforts.

Proceeding conceptually, we review the existing literature and practice of managerial social media use and delineate the challenges, or “tensions” professionals have to mitigate while expressing themselves online. This allows for a full exploration of digital interaction as a quest for equilibrium, between one’s professional and personal self-expression, but also between the management of one’s impression, and the emotional attachment to a social media profile.

We argue that social media may challenge current conceptions of managerial identity and work practices to a degree. Social media may demand different forms of representation both to inside and outside audiences, which can lead to the mediatization of both the professional and the organization, and call for a more conscious formulation of identity and management of impressions. We argue in particular that, within this context, online personae may serve as entities (through single or multiple accounts) delineating boundaries between the various roles managers are asked to perform within their professional and personal lives.

Managerial awareness toward a tool such as online personae may help in critically reflecting the embeddedness of managerial practice within social networks. A critical management of personae can also help in formulating identity-based strategies for gaining access and improving the quality of connections and interactions. Ultimately, as social media become a tool for workplace collaboration, the strategic thinking behind online personae might take a progressively larger importance for the success of individuals, and for organizations at large.

The chapter introduces a managerial point-of-view to the field of digital identities, widely analyzed on samples of adolescents and young adults. This allows to investigate matters proper of a professional life, such as the management of work/life boundaries, which become increasingly blurry in the online world. The chapter also introduces the concept of “online personae,” which aims at describing with more specificities the message and audience consequences behind the choice of one single social media profile, or several coexisting ones.

Following the mass closing of US schools during the COVID-19 pandemic of 2020, the authors noted an increase in discourse among literacy teachers and literacy coaches on social media platforms. Over a period of 9 months, the authors followed the interactions and work of social media scholars on the Twitter platform. In reflecting on Craig's (1995; Craig, Curtis, Kelly, Martindell, & Perez, 2020) illustrative pillars of knowledge communities and Brock's work on black cyberculture, we use narrative inquiry (Clandinin & Connelly, 2000; Connelly & Clandinin, 1990) to: (1) explore the elements of social media scholarship and (2) reflect on how active engagement in social media scholarship aids in the development of online knowledge communities that amplify and sustain the work of black womxn scholars.

Increasingly, diverse urban areas are in growing need of planning and design that include and welcome residents from a wide range of cultural and linguistic backgrounds. This study aims to use a service design framework to assess how the physical design of ten public spaces in New York City impacted visitors’ experience and sense of welcome.

The study used two main approaches to address this question. First, the authors conducted a sentiment analysis and a qualitative content analysis of Twitter data collected from each location as well as Tweets that mentioned each location. Second, they collected data through more traditional means by interviewing staff and visitors at four sites and also performing on site observational research.

These results suggested that certain physical design elements can significantly impact visitors’ sense of welcome and comfort. These include color, natural light, plentiful windows and open space. The study also found that Twitter data can be a useful tool to add a layer of insight into understanding visitors’ experience of a public building and recommends that public agencies should partner with cultural institutions and other community groups to use Twitter to monitor community attitudes and communicate with the public.

The research drew on the emerging service design framework and used novel data collection and analysis techniques.

Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were extensively publicized on social media, this study aims to analyse the temporal sentiments people express through tweets related to the war.

Relevant hashtag related to the Russia-Ukraine war was identified, and tweets were downloaded using Twitter API, which were later migrated to Orange Data mining software. Pre-processing techniques like transformation, tokenization, and filtering were applied to the extracted tweets. VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis module of Orange software was used to categorize tweets into positive, negative and neutral ones based on the tweet polarity. For ascertaining the key and co-occurring terms and phrases in tweets and also to visualize the keyword clusters, VOSviewer, a data visualization software, was made use of.

An increase in the number of tweets is witnessed in the initial days, while a decline is observed over time. Most tweets are negative in nature, followed by positive and neutral ones. It is also ascertained that tweets from verified accounts are more impactful than unverified ones. russiaukrainewar, ukraine, russia, false, war, nato, zelensky and stoprussia are the dominant co-occurring keywords. Ukraine, Russia and Putin are the top hashtags for sentiment representation. India, the USA and the UK contribute the highest tweets.

The study tries to explore the public sentiments expressed over Twitter related to Russia-Ukraine war.