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Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.

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 paper is to explore the effect of Elon Musk’s Twitter bio change on January 29, 2021 on the discourse around Bitcoin (BTC) on Twitter and to understand how these changes relate to the changes in Bitcoin price around that time.

This study implements sentiment analysis and text mining on Twitter data to explore changes in public sentiments toward Bitcoin after Elon Musk’s Twitter bio change. Furthermore, it uses Bitcoin price data obtained from the Binance exchange to understand its relation with Twitter discussion.

Elon Musk’s bio change on Twitter on January 29 increased the tweet volume mentioning Bitcoin. This increase in tweets had a strong positive correlation with Bitcoin price and preceded the rise in Bitcoin price. Although the bio change had an apparent effect on the tweet volume, there was no considerable effect on the tweet sentiments, indicating that tweet sentiment is a poor predictor of Bitcoin price.

This paper proposes an understanding of how social media influencers, like Elon Musk, affect the discourse around Bitcoin and can, in turn, have an impact on Bitcoin price.

Studies have shown a correlation and predictive impact of sentiment on asset prices, including Twitter sentiment on markets and individual stocks. This paper aims to determine whether there exists such a correlation between Twitter sentiment and property prices.

The authors construct district-level sentiment indices for every district of Istanbul using a dictionary-based polarity scoring method applied to a data set of 1.7 million original tweets that mention one or more of those districts. The authors apply a spatial lag model to estimate the relationship between Twitter sentiment regarding a district and housing prices or housing price appreciation in that district.

The findings indicate a significant but negative correlation between Twitter sentiment and property prices and price appreciation. However, the percentage of check-in tweets is found to be positively correlated with prices and price appreciation.

The analysis is cross-sectional, and therefore, unable to answer the question of whether Twitter can Granger-cause changes in housing markets. Future research should focus on creation of a property-focused lexicon and panel analysis over a longer time horizon.

The findings suggest a role for Twitter-derived sentiment in predictive models for local variation in property prices as it can be observed in real time.

This is the first study to analyze the link between sentiment measures derived from Twitter, rather than surveys or news media, on property prices.

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.

This paper delves into the discourse surrounding central bank digital currencies (CBDC) from the perspective of citizens’ views expressed on Twitter, on the potential implementation of a digital euro. The paper sheds light on citizens’ sentiments regarding CBDCs in relation to the euro and the euro area.

Using “TextBlob” sentiment analysis, this paper analyzes 4,462 Tweets containing the keywords “cbdc” and “euro” posted between September 14, 2018, and June 19, 2023. It explores citizens’ perceptions and concerns, as well as the general sentiment conveyed by the Tweets, through sentiment analysis and word-cloud generation. Text analysis and the “KMeans algorithm” are used to identify similar groups of Tweets. Statistical analysis of Term Frequency-Inverse Document Frequency (TF-IDF) keyword values in each cluster provides information about the relative distribution of words within clusters. In a further step, sentiment analysis is performed on each cluster by rating it positively, neutrally or negatively to identify the relevant dominant themes.

This paper reveals the evolving nature of CBDC-related discourse on Twitter over a four-year period, demonstrating a slight overall trend of positive sentiments. The distribution includes Tweets with a pronounced negative and positive sentiment, highlighting the need for clear and citizen-friendly communication through social media channels (such as Twitter or LinkedIn) in addition to the traditional channels used by official institutions.

The extraction of Tweets was limited to English; bilingual Tweets containing English words were also considered. Future research could be expanded to include Tweets in additional languages and compare them to English Tweets. Geographical data on the origin of the Tweets were not considered due to potential inaccuracies resulting from the use of VPNs.

The findings highlight citizens’ views, expectations and perceptions on the digital euro as a type of CBDC, suggesting ways in which the European Central Bank (ECB) can ensure customer satisfaction with its successful implementation.

The originality of the article revolves around the focus on the digital euro and on analyzing European citizens’ opinions regarding its implementation. In contrast to previous literature, this research discusses critically the pros and cons and possible design aspects of the new digital euro, by taking a citizen-centric approach and focusing solely on one type of currency. Technological advances and the digital era have left their mark on how society communicates and behaves. The implementation of CBDCs will affect the digital society. Therefore, the paper sparks a debate about European citizens’ sentiments toward adopting the digital euro, as a new digital currency, which will impact their lifestyle and financial decisions.

Destination monitoring is crucial to understand performance and identify key points of differentiation. Visitor satisfaction is an essential driver of destination performance. With the fast-growing volume of user-generated content through social media, it is now possible to tap into very large amounts of data provided by travellers as they share their experiences. Analysing these data for consumer sentiment has become attractive for destinations and companies. The idea of drawing on social media sentiment for satisfaction monitoring aligns well with the broader move towards smart destinations and real-time information processing. Thus, this paper aims to examine whether the electronic word of mouth originating from Twitter posts offers a useful source for assessing destination sentiment. Importantly, this research examines what caveats need to be considered when interpreting the findings.

This research focusses on a prominent tourist destination situated on Australia’s East Coast, the Gold Coast. Using a geographically informed filtering process, a collection of tweets posted from within the Gold Coast destination was created and analysed. Metadata were analysed to assess the population of Twitter users, and sentiment analysis, using the Valence Aware Dictionary for Sentiment Reasoning algorithm, was performed.

Twitter posts provide considerable information, including about who is visiting and what sentiment visitors and residents express when sending tweets from a destination. They also uncover some challenges, including the “noise” of Twitter data and the fact that users are not representative of the broader population, in particular for international visitors.

This paper highlights limitations such as lack of representativeness of the Twitter data, positive bias and the generic nature of many tweets. Suggestions for how to improve the analysis and value of tweets as a data source are made.

This paper contributes to understanding the value of non-traditional data sources for destination monitoring, in particular by highlighting some of the pitfalls of using information sources, such as Twitter. Further research steps have been identified, especially with a view to improving target-specific sentiment scores and the future employment of big-data approaches that involve integrating multiple data sources for destination performance monitoring.

The identification of cost-effective ways of measuring and monitoring guest satisfaction can lead to improvements in destination management. This in turn will enhance customer experience and possibly even resident satisfaction. The social benefits, especially at times of considerable visitation pressure, can be important.

The use of Twitter data for the monitoring of visitor sentiment at tourist destinations is novel, and the analysis presented here provides unique insights into the potential, but also the caveats, of developing new, smart systems for tourism.

目的地监控对理解绩效和确立区别关键点至关重要。游客满意是目的地绩效的关键动力。随着社交媒体上用户生成内容的快速增长, 研究其游客提供的大量数据变成可能, 这些数据体现了游客的旅游体验。分析这些消费者情绪的数据对目的地和有关企业的吸引力巨大。研究社交媒介情绪数据和满意度与更广泛地对智慧旅游和实时信息处理等方面的研究和谐一致。因此, 本论文旨在检验Twitter帖子中的在线口碑效应是否成为测量目的地情绪的有用数据。更重要的是, 本论文检验在研究结果中哪些领域应该着重考虑研究。

本论文集中研究了澳大利亚东海岸的一处旅游目的地, 黄金海岸。本论文使用地理信息过滤的处理方式, 有关黄金海岸的tweets为样本, 进行分析。本论文分析了元数据, 使用VADER数算, 检测了Twitter用户人口和情绪分析。

Twitter帖子提供相当多的信息, 包括谁是游客, 当游客发布有关旅游目的地的tweets的时候, 拥有什么样的情绪。研究结果还指出了一些挑战, 包括twitter数据的“杂音”, 用户并不能代表广大研究对象的事实, 特别是国际游客。

本论文强调了几点限制, 如Twitter数据的代表性、积极偏见、大多数tweets千篇一律等。本论文对如何提高分析结果和使用tweets作为数据源的价值提出了几点建议。

本论文对非传统数据以对旅游目的地监控的价值做出贡献, 尤其是强调了使用信息数据的弊端, 如Twitter。未来研究方向应该着重研究目标明确的情绪指数, 以及运用大数据分析方法, 分析多个数据源来检测旅游目的地性能。

本论文确立的经济有效的方法以衡量和监控游客满意度, 对提高目的地管理有着巨大帮助。同时, 这也可以提高游客体验和甚至提高当地居民的满意度。社会利益, 特别有的时候很大的旅游压力, 是巨大的。

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.

In this paper the authors present a study that uses Twitter to identify critical elements of customer service in the airline industry. The goal of the study was to uncover customer opinions about services by monitoring and analyzing public Twitter commentaries. The purpose of this paper is to identify elements of customer service that provide positive experiences to customers as well as to identify service processed and features that require further improvements.

The authors employed the approach of sentiment analysis as part of the netnography study. The authors processed 67,953 publicly shared tweets to identify customer sentiments about services of four airline companies. Sentiment analysis was conducted using the lexicon approach and vector-space model for assessing the polarity of Twitter posts.

By analyzing Twitter posts for their sentiment polarity the authors were able to identify areas of customer service that caused customer satisfaction, dissatisfaction as well as delight. Positive sentiments were linked mostly to online and mobile check-in services, favorable prices, and flight experiences. Negative sentiments revealed problems with usability of companies’ web sites, flight delays and lost luggage. Evidence of delightful experiences was recorded among services provided in airport lounges.

Paper demonstrates how sentiment analysis of Twitter feeds can be used in research on customer service experiences, as an alternative to Kano and SERVQUAL models.

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.