Public discourse and sentiment during Mpox outbreak: an analysis using natural language processing

Global health emergencies, such as the COVID-19 pandemic, have demonstrated the power and volatility of social media as a platform for public communication. Millions of people worldwide relied on digital platforms like Twitter and Facebook for real-time information, updates, and expressions of emotion. However, this rapid flow of information also led to the widespread diffusion of misinformation and changing patterns of discourse; a phenomenon that calls for a systematic investigation. This study explores topic evolution in social media discourse during global health emergencies using Natural Language Processing methods, including topic modelling (LDA and BERTopic), sentiment analysis (VADER and BERTweet), and geospatial trend analysis. A dataset of 70,452 COVID-19-related tweets collected from Nigeria between March 2020 and December 2022 was analyzed using Natural Language Processing (NLP) and data mining techniques. Topic modelling (Latent Dirichlet Allocation and BERTopic), sentiment analysis (VADER and BERTweet), and geospatial trend mapping were employed to examine the interplay between sentiment and thematic focus over time. The results reveal five dominant thematic clusters— public health awareness , lockdown and economy , vaccine and misinformation , recovery and hope , and political accountability . Temporal and spatial analyses further indicate that public sentiment and attention evolved in alignment with pandemic milestones such as lockdown enforcement, vaccine rollout, and policy controversies. The study contributes a hybrid analytical framework that integrates sentiment and topic evolution modelling for infodemic monitoring. By mapping discourse evolution across phases of a crisis, the research provides actionable insights for policymakers, communication strategists, and public health agencies to design adaptive and evidence-driven responses during global health emergencies.

Natural Language Processing (NLP) is an essential element of computational linguistics and artificial ‎intelligence, enabling fluid interactions between humans and machines. Social networking networks ‎produce substantial volumes of user-generated text daily, offering both opportunities and challenges for ‎NLP researchers. Social media discourse's informal, dynamic, and context-dependent characteristics ‎necessitate specific NLP techniques for precise processing and analysis. This study thoroughly examines NLP applications in social media, including essential tasks such as sentiment analysis, topic ‎modeling, misinformation detection, and hate speech identification. It examines the influence of machine ‎learning and deep learning methodologies, particularly transformer models, on the advancement of NLP ‎capabilities. This study also emphasizes the ethical issues related to NLP-driven social media apps, ‎including data privacy, algorithmic bias, and the regulation of misinformation. The paper continues by ‎discussing emerging research paths, highlighting the necessity for adaptable and ethical NLP solutions in ‎the changing social media environment‎.

In the face of increasing economic turbulence, understanding public discourse on sustainability has never been more crucial. This study dissects and comprehends the nuances of how the public perceives and discusses sustainability on social media during varying economic conditions. Leveraging the capabilities of Data Mining and Natural Language Processing (NLP), this research delves into the rich tapestry of Instagram posts to unearth sentiment trends, thematic patterns, and engagement levels related to sustainability. By systematically harvesting Instagram data, we aim to map out the fluctuations in public sentiment towards sustainability in relation to economic shifts. The research employs advanced NLP techniques, such as sentiment analysis, topic modelling, and semantic analysis, to process and interpret the vast array of user-generated content. The expected outcomes of this study include a detailed sentiment timeline correlating economic events with shifts in public opinion on sustainability, an understanding of key themes and topics that dominate discussions during different economic phases, and insights into the effectiveness of various sustainability narratives in engaging the public. This research not only contributes to academic discourse but also offers valuable insights for policymakers, environmental organizations, and social media strategists aiming to foster a more sustainable future in challenging economic times.

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

Natural language processing (NLP) has been changed by neural networks, which make it possible to analyze social media data in more detail, even though it is very large, not organized, and changes all the time. This abstract looks at how neural networks can be used in natural language processing (NLP) to analyze social media. Every day, social media sites produce huge amounts of written content that covers a wide range of topics, feelings, and writing styles. The subtleties and complexity of this data are often too much for traditional NLP methods to handle. Neural networks, on the other hand, offer strong answers because they can learn complex patterns and models from raw text. Sentiment analysis is one of the main ways that neural networks are used in social media research. Deep learning designs like recurrent neural networks (RNNs) or more advanced models like transformer-based architectures (e.g., BERT, GPT) can correctly describe how people feel about social media posts. Businesses can use this feature to find out what the public thinks, keep track of how people feel about their brand, and spot new trends in real time. Neural networks are useful for more than just analyzing mood. They can also help with named entity recognition (NER), topic modeling, and even figuring out humor and irony in text, which is hard for traditional rule-based systems because they rely on set rules and definitions. Also, neural networks are great at processing data in more than one language, which is important because social media is used all over the world. When learned on big datasets, models can be used across languages, giving us information about different language groups without a lot of human tweaking. Another big benefit of neural network methods is that they can be scaled up or down, which is very important for handling the huge amounts of data that social media sites create.

Social media platforms have become indispensable channels for communication and interaction, offering a wealth of data that can provide valuable insights into public sentiments. However, analyzing sentiment on these platforms poses significant challenges due to the diverse and unstructured nature of user-generated content. Traditional Natural Language Processing (NLP) techniques struggle to accurately classify sentiments expressed through text, images, emoticons, and multimedia elements. Moreover, the informal and nuanced language used in Electronic Word of Mouth (eWOM) further complicates sentiment analysis. In response, this paper explores the role of Artificial Intelligence (AI) in improving sentiment analysis on social media. By leveraging Machine Learning (ML) algorithms trained on large datasets, AI can enhance the accuracy and efficiency of sentiment classification, providing decision-makers with actionable insights into the sentiment landscape of social media. Keywords: Social media, Sentiment analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Electronic Word of Mouth.

BackgroundLoneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18–70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon. ObjectivesThis paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed. MethodsThis study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations. ResultsThe analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue. ConclusionThis paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.

The COVID-19 pandemic has caused many entrepreneurs and small and medium enterprises (SMEs) to adapt their business models and business strategies to the consequences caused by the pandemic. In order to identify the main innovations and technologies adopted by SMEs in the pandemic, in the present study, we used a database of 56,941 tweets related to the coronavirus to identify those that contained the hashtag #SMEs. The final sample was analyzed using several data-mining techniques such as sentiment analysis, topic modeling and textual analysis. The theoretical perspectives adopted in the present study were Computer-Aided Text Analysis, User-Generated Content and Natural Language Processing. The results of our analysis helped us to identify 15 topics (7 positive: Free support against Covid-19, Webinars tools, Time Optimizer and efficiency, Business solutions tools, Advisors tools, Software for process support and Back-up tools; 4 negative: Government support, Payment systems, Cybersecurity problems and Customers solutions in Cloud, and and 4 neutral: Social media and e-commerce, Specialized startups software, CRMs and Finance and Big data analysis tools). The results of the present study suggest that SMEs have used a variety of digital tools and strategies to adapt to the changing market conditions brought on by the pandemic, and have been proactive in adopting new technologies to continue to operate and reach customers in a connected era. Future research should be directed towards understanding the long-term effects of these technologies and strategies on entrepreneurial growth and value creation, as well as the sustainability of SMEs in the new era based on data-driven decisions.

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

A Review on Reddit News Headlines with NLTK tool

BackgroundMeasuring public response during COVID-19 is an important way of ensuring the suitability and effectiveness of epidemic response efforts. An analysis of social media provides an approximation of public sentiment during an emergency like the current pandemic. The measures introduced across the globe to help curtail the spread of the coronavirus have led to the development of a situation labeled as a “perfect storm,” triggering a wave of domestic violence. As people use social media to communicate their experiences, analyzing public discourse and sentiment on social platforms offers a way to understand concerns and issues related to domestic violence during the COVID-19 pandemic.ObjectiveThis study was based on an analysis of public discourse and sentiment related to domestic violence during the stay-at-home periods of the COVID-19 pandemic in Australia in 2020. It aimed to understand the more personal self-reported experiences, emotions, and reactions toward domestic violence that were not always classified or collected by official public bodies during the pandemic.MethodsWe searched social media and news posts in Australia using key terms related to domestic violence and COVID-19 during 2020 via digital analytics tools to determine sentiments related to domestic violence during this period.ResultsThe study showed that the use of sentiment and discourse analysis to assess social media data is useful in measuring the public expression of feelings and sharing of resources in relation to the otherwise personal experience of domestic violence. There were a total of 63,800 posts across social media and news media. Within these posts, our analysis found that domestic violence was mentioned an average of 179 times a day. There were 30,100 tweets, 31,700 news reports, 1500 blog posts, 548 forum posts, and 7 comments (posted on news and blog websites). Negative or neutral sentiment centered on the sharp rise in domestic violence during different lockdown periods of the 2020 pandemic, and neutral and positive sentiments centered on praise for efforts that raised awareness of domestic violence as well as the positive actions of domestic violence charities and support groups in their campaigns. There were calls for a positive and proactive handling (rather than a mishandling) of the pandemic, and results indicated a high level of public discontent related to the rising rates of domestic violence and the lack of services during the pandemic.ConclusionsThis study provided a timely understanding of public sentiment related to domestic violence during the COVID-19 lockdown periods in Australia using social media analysis. Social media represents an important avenue for the dissemination of information; posts can be widely dispersed and easily accessed by a range of different communities who are often difficult to reach. An improved understanding of these issues is important for future policy direction. Heightened awareness of this could help agencies tailor and target messaging to maximize impact.

This study analyses the topic of stress and anxiety in 3,765 Reddit posts to determine key themes and emotional undertones using natural language processing (NLP) techniques. Five major category topics are identified from the posts using the latent Dirichlet allocation (LDA) algorithm. The topics identified are general discontent and lack of direction; panic and anxiety attacks; physical symptoms of anxiety, stress, and mental health concerns; and seeking help for anxiety. Sentiment analysis with the help of TextBlob showed a neutral score, for the most part: an average polarity score of 0.009 and a subjectivity score of 0.494. Several kinds of visualizations, including word clouds, bar charts, and pie charts, have been used to show the distribution and importance of these topics. These findings underscore the important role played by online communities in extending their support to those in distress because of mental health problems. This information is very important to mental health professionals and researchers. This study shows the effectiveness of using a combination of topic modeling and sentiment analysis to identify problems related to mental health discussed on social media. These results direct the possibilities for future research in using advanced NLP techniques and expanding to larger datasets.

Toward reduction of detrimental effects of hurricanes using a social media data analytic Approach: How climate change is perceived?

Generative AI platforms like ChatGPT have leapfrogged in terms of technological advancements. Traditional methods of scrutiny are not enough for assessing their technological efficacy. Understanding public sentiment and feelings towards ChatGPT is crucial for pre-empting the technology’s longevity and impact while also providing a silhouette of human psychology. Social media platforms have seen tremendous growth in recent years, resulting in a surge of user-generated content. Among these platforms, Reddit stands out as a forum for users to engage in discussions on various topics, including Generative Artificial Intelligence (GAI) and chatbots. Traditional pedagogy for social media sentiment analysis and opinion mining are time consuming and resource heavy, while lacking representation. This paper provides a novice multifrontal approach that utilises and integrates various techniques for better results. The data collection and preparation are done through the Reddit API in tandem with multi-stage weighted and stratified sampling. NLP (Natural Language processing) techniques encompassing LDA (Latent Dirichlet Allocation), Topic modelling, STM (Structured Topic Modelling), sentiment analysis and emotional analysis using RoBERTa are deployed for opinion mining. To verify, substantiate and scrutinise all variables in the dataset, multiple hypothesises are tested using ANOVA, T-tests, Kruskal–Wallis test, Chi-Square Test and Mann–Whitney U test. The study provides a novel contribution to the growing literature on social media sentiment analysis and has significant new implications for discerning user experience and engagement with AI chatbots like ChatGPT.

COVID-19 is a global health emergency that has fundamentally altered human life. Public perception about COVID-19 greatly informs public policymaking and charts the course of present and future mitigation strategies. Existing approaches to gain insights into the evolving nature of public opinion has led to the application of natural language processing on public interaction data acquired from online surveys and social media. In this work, we apply supervised and unsupervised machine learning approaches on global Twitter data to learn the opinions about adoption of mitigation strategies such as social distancing, masks, and vaccination, as well as the effect of socioeconomic, demographic, political, and epidemiological features on perceptions. Our study reveals the uniform polarity in public sentiment on the basis of spatial proximity or COVID-19 infection rates. We show the reservation about the adoption of social distancing and vaccination across the world and also quantify the influence of airport traffic, homelessness, followed by old age and race on sentiment of netizens within the US.