Themes Of Tweets Research Articles

From Optimism to Concern: Unveiling Sentiments and Perceptions Surrounding ChatGPT on Twitter

Artificial intelligence (AI) technologies, as a product of processes aimed at imitating human intelligence through computers and software, affect our daily lives in various aspects, including cultural, technological, and economic. The advent of ChatGPT, developed by OpenAI, signifies a pivotal AI advancement in human language comprehension and generation, heralding profound implications across societal, economic, and cultural dimensions. However, a notable gap exists in scholarly literature concerning the examination of perceptions and discussions surrounding ChatGPT. This study aims to address this gap by analyzing Twitter conversations, comprising 1.1 million tweets containing “ChatGPT” or “#ChatGPT” between December 1 2022 and June 1 2023, with geolocation data. Employing a text as data approach encompassing text, sentiment, and semantic network analyses, sentiment polarity and lexical patterns were explained, while semantic network analysis revealed central expressions and prominent themes in Twitter discussions. The study highlights the social effects of AI technologies from the perspective of Twitter users and reveals the sentimental tendency and themes in tweets with geographical and economic dimensions. The findings reflect the prevalence of positive sentiment and hype toward ChatGPT while there are also concerns regarding privacy, cybersecurity, and misuse of AI tools. More importantly, content generation, technical aspects, business applications, educational use, and competitors are among the main themes of ChatGPT-related discussions on Twitter.

Clustering and Analysis of Tweets Related to Petrobras

This study aimed to cluster and analyze tweets associated with Petrobras, exploring its meaning and user profiles on social media to understand their impact on financial markets. The research applied a workflow including the data collection from Twitter's API (current X), preprocessing of tweets using Python libraries, word vectorization via Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), Principal Component Analysis (PCA) to reduce matrix dimensionality, and the K-means clustering technique. A total of 840 preprocessed tweets were clustered and analyzed for patterns related to Petrobras. Five clusters were identified in the initial analysis with no dimensionality reduction, showcasing differing characteristics, while the subsequent PCA-based analysis yielded three clusters showing contrasting themes in tweets. The PCA-based analysis showed grouped tweets about the market and economy (cluster 0), while cluster 1 was related to political concerns. Limitations included reliance on publicly available Twitter data, constraints due to the quantity and nature of tweets, and potential biases in sentiment analysis due to informal language and sarcasm. The research underscores the potential of unsupervised machine learning techniques in analyzing sentiments and user profiles related to financial markets. Insights derived from tweet clustering could aid investors in gauging market sentiment.

HPV vaccine narratives on Twitter during the COVID-19 pandemic: a social network, thematic, and sentiment analysis

IntroductionThe COVID-19 pandemic has increased online interactions and the spread of misinformation. Some researchers anticipate benefits stemming from improved public awareness of the value of vaccines while others worry concerns around vaccine development and public health mandates may have damaged public trust. There is a need to understand whether the COVID-19 pandemic, vaccine development, and vaccine mandates have influenced HPV vaccine attitudes and sentiments to inform health communication strategies.MethodsWe collected 596,987 global English-language tweets from January 2019-May 2021 using Twitter’s Academic Research Product track. We determined vaccine confident and hesitant networks discussing HPV immunization using social network analysis. Then, we used a neural network approach to natural language processing to measure narratives and sentiment pertaining to HPV immunization.ResultsMost of the tweets in the vaccine hesitant network were negative in tone (54.9%) and focused on safety concerns surrounding the HPV vaccine while most of the tweets in the vaccine confident network were neutral (51.6%) and emphasized the health benefits of vaccination. Growth in negative sentiment among the vaccine hesitant network corresponded with legislative efforts in the State of New York to mandate HPV vaccination for public school students in 2019 and the WHO declaration of COVID-19 as a Global Health Emergency in 2020. In the vaccine confident network, the number of tweets concerning the HPV vaccine decreased during the COVID-19 pandemic but in both vaccine hesitant and confident networks, the sentiments, and themes of tweets about HPV vaccine were unchanged.ConclusionsAlthough we did not observe a difference in narratives or sentiments surrounding the HPV vaccine during the COVID-19 pandemic, we observed a decreased focus on the HPV vaccine among vaccine confident groups. As routine vaccine catch-up programs restart, there is a need to invest in health communication online to raise awareness about the benefits and safety of the HPV vaccine.

A framework for recommending health-related topics based on topic modeling in conversational data (Twitter)

Social media is an environment where it enables the user’s to create and share content or participate in all social networking activities; it has given rise to information overloading problem and knowledge starvation. Generally, in large conversational dataset like Twitter it is very difficult to identifying public health-related topics. Machine learning is the area which helps us to overcome this situation. By applying probabilistic unsupervised machine learning we have identified latent topics from the text corpora. Topic modeling algorithms are probabilistic generative models which is used to analyze the words of the original texts to identify themes that run through them, how connected to each other by themes, and how the topics change over (Blei in Commun ACM 55(4):77–84, 2012). In this paper we have proposed a framework for recommending health-related topics by applying an enhanced topic model hierarchical latent Dirichlet allocation for identifying public health-related topics and themes in tweets. We have tested our model by exploring tobacco usage across conversational data, as well as tested with the subset of data generated by tobacco related queries. Our test results indicate most of the public health related issues are uncovered in our model. However, this model provides relevant topic across the tobacco subset.

Motives for sharing illness experiences on Twitter: conversations of parents with children diagnosed with cancer

ABSTRACTA patient- and family-centred approach in paediatric health care is important because parents are involved in making key decisions about their child’s health care and advocating for the best interest of the child. Parents and family members are increasingly turning to the internet to find and actively share information about their child’s health care. Twitter is one of many online platforms used by parents of children diagnosed with cancer to share information related to their child’s cancer experience. Existing research suggests that there is a need to better understand the motives for using Twitter for sharing content about a child’s cancer experience. Furthermore, there is a lack of theoretical frameworks for characterizing those motives. In this paper, we identify key themes of tweets posted by parents of children diagnosed with cancer and align those themes with motives inspired by the well-studied Everyday Life Information Seeking framework. We propose a new motive in addition to those associated with the framework and suggest that information can be shared for endogenous reasons as well as to meet the needs of others. This paper contributes an increased understanding of motives for sharing information about a child’s cancer journey and extends a theoretical framework for building further knowledge in this area.