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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.

The purpose of this study is to investigate how libraries use Twitter as a social media platform and examine the tweets they post, including multimedia content such as images and video clips. The study also aims to analyse the relationship between post types and user engagement and evaluate the effects of post features, such as multimedia content, on user engagement.

The methodology of the study involved three phases. In Phase 1, a review of related literature was conducted to develop a holistic approach for the study. In Phase 2, official Twitter handles of selected libraries were identified and verified for authenticity using various methods, including cross-checking with library websites. During Phase 3, data was collected from the Twitter handles. The data was then tabulated and interpreted to achieve the set objectives of the study.

The paper examined the tweets posted by select libraries on Twitter and their impact on user engagement. The study found that most tweets were related to library resources/collection and announcements, followed by events hosted by libraries. Emotionally inspiring posts and daily facts were also commonly posted. The findings also showed that including images in tweets resulted in higher levels of user engagement than video clips did. The study suggests that incorporating images fosters engagement and boosts retweets, while watching a video takes more effort and time.

The practical implications of the study can provide insights into the tweets that generate user engagement, which can help libraries tailor their social media strategies to attract and retain more followers. The paper can help libraries measure the success of their social media activities by evaluating user engagement metrics.

The originality/ value of the study lies in its examination of how libraries use Twitter as a social media platform, including the tweets they post and the impact of multimedia content on user engagement. While previous studies have examined the use of social media by libraries, this study focuses specifically on Twitter and provides a detailed analysis of the tweets that generate user engagement.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

The study aims to access, monitor and visualize the scientific progress of Twitter-based research through a bibliometric analysis of scientific publications.

The data was retrieved from 2006 to February 23, 2022 using the Web of Science, a leading indexing and abstracting database. In response to the authors’ query, 6,193 items with 101,037 citations, an average citation of 16.31 and an h index of 126 were received. The “Biblioshiny” extension of the “Bibliometrics” package (www.bibliometrix.org) of R software was used to evaluate and visualize the data.

The present study highlighted the scientific progress of the field evolved over a period of time. The obtained results uncovered the publication trends, productive countries and their collaboration pattern, active authors who nurture the field by making their contribution, prolific source titles adopted by authors to publish the literature on the topic, most productive language in which literature was written, productive institutions, funding agencies that sponsor the research, influential articles, prominent keywords used in publications were also identified which will aid scientists in identifying research gaps in a particular area.

This study comprehensively illustrates the research status of Twitter-related research by conducting a bibliometric analysis. The study’s findings can assist relevant researchers in understanding the research trend, seeking scientific collaborators and funding for their research. Further, the study will act as a ready reference tool for the scientific community to identify research gaps, select research topics and appropriate platforms for submitting their scholarly endeavors.

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.

This study explored how the use of different social media is related to subjective well-being among university students during the COVID-19 pandemic in Japan.

We surveyed 1,681 university students in the Kanto region of Japan in May 2021 to investigate how social media use relates to subjective well-being. We also examined the effects of self-consciousness and friendship, self-presentation desire, generalized trust, online communication skills, depression tendency and social support from others.

The responses revealed 15 possible patterns of social media usage on four widely used social media in Japan (LINE, Twitter, Instagram and Facebook). We selected users with the top five patterns for further statistical analyses: LINE/Twitter/Instagram/Facebook, LINE/Twitter/Instagram, LINE/Twitter, LINE/Instagram and LINE only. Overall, self-establishment as a factor of self-consciousness and friendship, and social support from others had positive effects on the improvement of subjective well-being, whereas depression tendency had negative effects on their subjective well-being regardless of their usage patterns, of which the results of social support from others and depression tendency were consistent with the results of previous studies. Regarding other factors, they had different effects on subjective well-being due to different patterns. Effects on subjective well-being from self-indeterminate and self-independency as factors of self-consciousness and friendship, praise acquisition, self-appeal and topic avoidance as factors of self-presentation desire were observed.

This is among the earliest studies on the relationship between young generations’ social media use and subjective well-being through social media usage patterns during the COVID-19 pandemic in Japan.

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.

This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.

Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.

This study implements in Indonesia region.

(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

This study aims to reveal how the COVID-19 vaccine was accepted in the Japanese Twitter-sphere. This study explores how the topics related to the vaccine promotion project changed on Twitter and how the topics that were likely to spread changed during the vaccine promotion project.

The computational social science methodology was adopted. This study collected all tweets containing the word “vaccine” using the Twitter API from March to October 2021 and conducted the following analysis: analyzing frequent words and identifying topics likely to spread through the cosine similarity and Tobit model.

First, vaccine hesitancy–related words were frequently mentioned during the vaccine introduction and dissemination periods and had diffusing power only during the former period. Second, vaccine administration–related words were frequently mentioned and diffused through April to May and had diffusing power throughout the period. The background to these findings is that the sentiment of longing for vaccines outweighed that of hesitancy toward vaccines during this period.

This study finds that the timing of the rise in vaccine hesitation sentiment and the timing of the start of vaccine supply were misaligned. This is one of the reasons that Japan, which originally exhibited strong vaccine hesitancy, did not face vaccine hesitancy in the COVID-19 vaccine promotion project.

Gathering knowledge regarding personality traits has long been the interest of academics and researchers in the fields of psychology and in computer science. Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies).

In this, cleaning the dataset and extracting relevant potential features “as assessed by psychological experts” are essential, as Indonesians tend to mix formal words, non-formal words, slang and abbreviations when writing social media posts. For this article, raw data were derived from a predefined dominance, influence, stability and conscientious (DISC) quiz website, returning 316,967 tweets from 1,244 Twitter accounts “filtered to include only personal and Indonesian-language accounts”. Using a combination of NLP techniques and machine learning, the authors aim to develop a better approach and more robust model, especially for the Indonesian language.

The authors find that employing a SMOTETomek re-sampling technique and hyperparameter tuning boosts the model’s performance on formalized datasets by 57% (as measured through the F1-score).

The process of cleaning dataset and extracting relevant potential features assessed by psychological experts from it are essential because Indonesian people tend to mix formal words, non-formal words, slang words and abbreviations when writing tweets. Organic data derived from a predefined DISC quiz website resulting 1244 records of Twitter accounts and 316.967 tweets.