ChatGPT is used most widely among those between 25 and 34 around the world. The youngest group, those under 24, are the second largest userbase, and together those under 34 account for over 60 percent of ChatGPT users. It is perhaps unsurprising that the younger age brackets use the chatbot more than older as that is the common trend with new technologies. Male users were far more numerous than female users, with males representing over 65 percent of total users in 2023.

ChatGPT has taken the world by storm, setting a record for the fastest app to reach a 100 million users, which it hit in two months. The implications of this tool are far-reaching, universities...

This dataset presents ChatGPT usage patterns across different age groups, showing the percentage of users who have followed its advice, used it without following advice, or have never used it, based on a 2025 U.S. survey.

As of June 2023, it was reported that **** percent of employees of worldwide companies have tried using ChatGPT in the workplace at least once. Those who have put confidential corporate data into the AI-powered tool were *** percent.

This dataset contains information related to ChatGPT, OpenAI's conversational AI model, gathered from social media. It includes keywords such as "chatgpt" and "chat gpt", as well as associated hashtags and mentions. The dataset's purpose is to help in understanding public opinion, identifying trends, and exploring potential applications of ChatGPT. By analysing tweet volume, sentiment, user engagement, and the influence of key AI events, this dataset offers valuable insights for various stakeholders.

Columns

• date: The date and time when the tweet was posted.
• id: A unique identifier for each tweet.
• content: The text of the tweet.
• username: The Twitter username of the person who posted the tweet.
• like_count: The number of likes received by the tweet.
• retweet_count: The number of times the tweet was retweeted.

Distribution

This dataset is provided as a CSV file and includes data on 500,000 tweets. The dataset consists of two CSV files: an originally scraped dataset and a preprocessed dataset.

Usage

This dataset is ideal for: * Understanding public sentiment and trends surrounding ChatGPT. * Analysing tweet volume and user engagement related to AI-powered conversational technologies. * Exploring the influence of key AI events on social media discussions. * Supporting research into the societal impact and adoption of conversational AI.

Coverage

The dataset covers the period from January to March 2023. The data collected is global in scope, capturing public opinion on social media platforms.

License

CC0

Who Can Use It

• Companies: To inform product development, marketing strategies, and market positioning within the AI sector.
• Researchers: For academic studies on public perception of AI, natural language processing applications, and social media dynamics.
• Policymakers: To understand societal implications of AI technologies and inform regulatory frameworks.

Dataset Name Suggestions

• ChatGPT Twitter Data Jan-Mar 2023
• AI Conversational Model Tweets (Early 2023)
• Public Sentiment on ChatGPT
• Social Media Trends: ChatGPT Activity

Attributes

Original Data Source: 500k ChatGPT-related Tweets Jan-Mar 2023

A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N=1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022). Guided by a model of technology acceptance (UTAUT-2), we examine the role of socio-demographics commonly associated with the digital divide in ChatGPT activity and explore further socio-political attributes identified via stability selection in Lasso regressions. We confirm that lower age and higher education affect ChatGPT usage, but neither gender nor income do. We find full-time employment and more children to be barriers to ChatGPT activity. Using a variety of social media was positively associated with ChatGPT activity. In terms of political variables, political knowledge and political self-efficacy as well as some political behaviors such as voting, debating political issues online and offline and political action online were all associated with ChatGPT activity, with online political debating and political self-efficacy negatively so. Finally, need for cognition and communication skills such as writing, attending meetings, or giving presentations, were also associated with ChatGPT engagement, though chairing/organizing meetings was negatively associated. Our research informs efforts to address digital disparities and promote digital literacy among underserved populations by presenting implications, recommendations, and discussions on ethical and social issues of our findings.

Within a year of its launch, ChatGPT has seen a surge in popularity. While many are drawn to its effectiveness and user-friendly interface, ChatGPT also introduces moral concerns, such as the temptation to present generated text as one’s own. This led us to theorize that personality traits such as Machiavellianism and sensation-seeking may be predictive of ChatGPT usage. We launched two online questionnaires with 2,000 respondents each, in September 2023 and March 2024, respectively. In Questionnaire 1, 22% of respondents were students, and 54% were full-time employees; 32% indicated they used ChatGPT at least weekly. Analysis of our ChatGPT Acceptance Scale revealed two factors, Effectiveness and Concerns, which correlated positively and negatively, respectively, with ChatGPT use frequency. A specific aspect of Machiavellianism (manipulation tactics) was found to predict ChatGPT usage. Questionnaire 2 was a replication of Questionnaire 1, with 21% students and 54% full-time employees, of which 43% indicated using ChatGPT weekly. In Questionnaire 2, more extensive personality scales were used. We found a moderate correlation between Machiavellianism and ChatGPT usage (r = .22) and with an opportunistic attitude towards undisclosed use (r = .30), relationships that largely remained intact after controlling for gender, age, education level, and the respondents’ country. We conclude that covert use of ChatGPT is associated with darker personality traits, something that requires further attention.

This dataset shows how men and women in the U.S. reported using ChatGPT in a 2025 survey, including whether they followed its advice or chose not to use it.

This dataset presents ChatGPT usage patterns across U.S. Census regions, based on a 2025 nationwide survey. It tracks how often users followed, partially used, or never used ChatGPT by state region.

This dataset presents how much users trust ChatGPT across different advice categories, including career, education, financial, legal, and medical advice, based on a 2025 U.S. survey.

Input and output data from long-term learning in mathematical statistics using ChatGPT

This dataset shows the percentage of U.S. adults who say they trust ChatGPT more than a human expert, based on a 2025 national AI trust survey.

This dataset shows the types of advice users sought from ChatGPT based on a 2025 U.S. survey, including education, financial, medical, and legal topics.

The potential of using Chat GPT and AI to revolutionize the way we interact with computers, specifically in the field of medical diagnostics. Chat GPT can make conversations between doctors and patients more natural, while AI can analyze vast amounts of patient data to identify trends and estimate a patient’s health. Patients can use Chat GPT to better understand their medical conditions, and both Chat GPT and AI can be used to automate tasks such as scheduling appointments and processing test results. However, there are limitations to using AI, including data bias, complex results, and analysis errors. To reduce errors, it is important to validate findings using various techniques and ensure that data is accurate and up-to-date. Chat GPT also employs security measures to protect patient data privacy and confidentiality.

In January 2023, ChatGPT registered over nine million interactions from users in Italy, up by over 300 percent compare to the previous month. By comparison, the OpenAI website registered 1.2 million actions performed by Italian users. At the end of March 2023, the main national privacy regulator in Italy prompted OpenAI to provide information on how and why the company collects user data, if the company wanted to avoid seeing its access to the Italian market blocked.

This dataset provides user reviews for ChatGPT, offering valuable qualitative feedback, satisfaction ratings, and submission dates. It captures a diverse array of user sentiments, from concise remarks to more detailed feedback. The ratings are provided on a scale of 1 to 5, indicating different levels of user satisfaction. The dataset spans several months, which allows for temporal analysis of sentiment trends, as each review includes a timestamp. This data is ideal for gaining insights into user characteristics and for improving application features and services.

Columns

• Review Id: A unique identifier for each individual review. This is formatted as a String, typically in a UUID structure.
• Review: The actual text of the user's feedback, offering qualitative insights into their experience with the application. This is a String data type.
• Ratings: User-submitted numerical ratings, ranging from 1 (lowest satisfaction) to 5 (highest satisfaction), indicating their level of contentment. This is an Integer data type.
• Review Date: The timestamp when the review was originally submitted, recorded in MM/DD/YYYY HH:MM format, serving as a Date_Time data type.

Distribution

The dataset is provided as a free resource. While a sample file will be updated separately to the platform, the data quality is assessed as 5 out of 5, with the current version being 1.0. It was listed on 08/06/2025, with 1 view and 0 downloads recorded so far. The dataset contains approximately 193,154 unique reviews.

Usage

This dataset is particularly useful for various analytical applications, including: * Sentiment Analysis: Developing models to predict the emotional tone or sentiment conveyed in user reviews. * Customer Feedback Analysis: Extracting actionable insights that can inform and guide improvements to application features and services. * Review Classification: Building machine learning models to categorise user reviews, for instance, as positive or negative. * Data Visualisation: Creating visual representations of review patterns and trends. * Exploratory Data Analysis: Investigating the characteristics and underlying patterns within the review data. * Natural Language Processing (NLP): Applying NLP techniques to understand and process the textual feedback. * Text Mining: Discovering patterns and insights from the large collection of text reviews. * Time-Series Analysis: Examining how sentiment and ratings evolve over time based on review timestamps.

Coverage

This dataset comprises user reviews for ChatGPT collected from 25th July 2023 to 24th August 2024. The data collection is global, reflecting feedback from users worldwide.

License

CCO

Who Can Use It

This dataset is ideal for a range of users interested in understanding user feedback and sentiment, including: * Data Scientists and Machine Learning Engineers for building and training sentiment analysis and classification models. * Product Managers and App Developers to gain actionable insights for product improvement and feature development. * Market Researchers to understand user satisfaction and market perception of AI applications. * Academic Researchers studying human-computer interaction, natural language processing, or user behaviour.

Dataset Name Suggestions

• ChatGPT User Reviews
• GPT User Review Sentiment Data
• AI App User Feedback Dataset
• ChatGPT User Satisfaction Ratings

Attributes

Original Data Source: ChatGPT Users Reviews

According to the survey conducted in May 2023 in Taiwan, nearly 74 percent of internet users had never used ChatGPT in the past three months. In comparison, 4.6 percent of users had often or always used ChatGPT.

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Chatbot Market Size 2025-2029

The chatbot market size is forecast to increase by USD 9.63 billion, at a CAGR of 42.9% between 2024 and 2029.

The market is witnessing significant growth, driven by the integration of chatbots with various communication channels such as social media, websites, and messaging apps. This integration enables businesses to engage with customers in real-time, providing instant responses and enhancing customer experience. However, the market faces challenges, including the lack of awareness and standardization of chatbot services. Despite these obstacles, the potential benefits of chatbots, including cost savings, increased efficiency, and improved customer engagement, make it an attractive investment for businesses seeking to enhance their digital presence and streamline operations. Companies looking to capitalize on this market opportunity should focus on developing chatbot solutions that offer customizable features, seamless integration with existing systems, and natural language processing capabilities to deliver human-like interactions. Navigating the challenges of awareness and standardization will require targeted marketing efforts and collaborations with industry partners to establish best practices and industry standards.

What will be the Size of the Chatbot Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with dynamic market dynamics shaping its growth and applications across various sectors. Conversational AI, a key component of chatbots, is advancing with the integration of sentiment analysis, emotional intelligence, and meteor score to enhance user experience. Pre-trained models and language understanding are being utilized to improve performance metrics, while neural networks and contextual awareness enable more accurate intent recognition. Deployment strategies, including policy learning and cloud platforms, are evolving to support cross-platform compatibility and multi-lingual support. Performance metrics, such as F1-score and response time, are crucial in evaluating model effectiveness. Reinforcement learning and knowledge base integration are essential for chatbot development and lead generation. Error rate and character error rate are critical in speech recognition, while API integration and dialogue state tracking facilitate seamless conversational experiences. Technical support and customer engagement are primary applications of chatbots, with sales conversion and automated responses optimizing business operations. Deep learning architectures and transfer learning are driving advancements in question answering and natural language processing. Contextualized word embeddings and dialogue management are essential for effective user interaction. Overall, the market is an ever-evolving landscape, with continuous innovation and integration of advanced technologies shaping its future.

How is this Chatbot Industry segmented?

The chatbot industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userRetailBFSIGovernmentTravel and hospitalityOthersProductSolutionsServicesDeploymentCloud-BasedOn-PremiseHybridApplicationCustomer ServiceSales and MarketingHealthcare SupportE-Commerce AssistanceGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKMiddle East and AfricaEgyptKSAOmanUAEAPACChinaIndiaJapanSouth AmericaArgentinaBrazilRest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth, particularly in the retail sector. E-commerce giants like Amazon, Flipkart, Alibaba, and Snapdeal are leading this trend, integrating chatbots to improve customer experience during online product searches. These AI-powered bots facilitate quick and effective resolution of payment-related queries, enhancing the shopping experience. However, retailers face challenges in ensuring a seamless user experience, as consumers increasingly prefer mobile shopping. Deep learning architectures and natural language processing (NLP) are crucial components of chatbot development. NLP enables intent recognition, sentiment analysis, and entity extraction, while deep learning models provide contextual awareness and dialogue management. Speech recognition and dialogue state tracking further enhance the user experience. Cross-platform compatibility and multi-lingual support are essential features for chatbots, catering to diverse user bases. Pre-trained models and transfer learning enable faster development and deployment. Reinforcement learning and policy learning optimize bot

The data set records the perceptions of Bangladeshi university students on the influence that AI tools, especially ChatGPT, have on their academic practices, learning experiences, and problem-solving abilities. The varying role of AI in education, which covers common usage statistics, what AI does to our creative abilities, its impact on our learning, and whether it could invade our privacy. This dataset reveals perspective on how AI tools are changing education in the country and offering valuable information for researchers, educators, policymakers, to understand trends, challenges, and opportunities in the adoption of AI in the academic contex.

Methodology Data Collection Method: Online survey using google from Participants: A total of 3,512 students from various Bangladeshi universities participated. Survey Questions:The survey included questions on demographic information, frequency of AI tool usage, perceived benefits, concerns regarding privacy, and impacts on creativity and learning.

Sampling Technique: Random sampling of university students Data Collection Period: June 2024 to December 2024

Privacy Compliance This dataset has been anonymized to remove any personally identifiable information (PII). It adheres to relevant privacy regulations to ensure the confidentiality of participants.

For further inquiries, please contact: Name: Md Jhirul Islam, Daffodil International University Email: jhirul15-4063@diu.edu.bd Phone: 01316317573

Objective: Our objective is to evaluate the efficacy of ChatGPT 4 in accurately and effectively delivering genetic information, building on previous findings with ChatGPT 3.5. We focus on assessing the utility, limitations, and ethical implications of using ChatGPT in medical settings. Materials and Methods: A structured questionnaire, including the Brief User Survey (BUS-15) and custom questions, was developed to assess ChatGPT 4's clinical value. An expert panel of genetic counselors and clinical geneticists independently evaluated ChatGPT 4's responses to these questions. We also involved comparative analysis with ChatGPT 3.5, utilizing descriptive statistics and using R for data analysis. Results: ChatGPT 4 demonstrated improvements over 3.5 in context recognition, relevance, and informativeness. However, performance variability and concerns about the naturalness of the output were noted. No significant difference in accuracy was found between ChatGPT 3.5 and 4.0. Notably, the efficacy of ChatGPT 4 varied significantly across different genetic conditions, with specific differences identified between responses related to BRCA1 and HFE. Discussion and Conclusion: This study highlights ChatGPT 4's potential in genomics, noting significant advancements over its predecessor. Despite these improvements, challenges remain, including the risk of outdated information and the necessity of ongoing refinement. The variability in performance across different genetic conditions underscores the need for expert oversight and continuous AI training. ChatGPT 4, while showing promise, emphasizes the importance of balancing technological innovation with ethical responsibility in healthcare information delivery. Methods Study Design This study was conducted to evaluate the performance of ChatGPT 4 (March 23rd, 2023) Model) in the context of genetic counseling and education. The evaluation involved a structured questionnaire, which included questions selected from the Brief User Survey (BUS-15) and additional custom questions designed to assess the clinical value of ChatGPT 4's responses. Questionnaire Development The questionnaire was built on Qualtrics, which comprised twelve questions: seven selected from the BUS-15 preceded by two additional questions that we designed. The initial questions focused on quality and answer relevancy: 1. The overall quality of the Chatbot’s response is: (5-point Likert: Very poor to Very Good) 2. The Chatbot delivered an answer that provided the relevant information you would include if asked the question. (5-point Likert: Strongly disagree to Strongly agree) The BUS-15 questions (7-point Likert: Strongly disagree to Strongly agree) focused on: 1. Recognition and facilitation of users’ goal and intent: Chatbot seems able to recognize the user’s intent and guide the user to its goals. 2. Relevance of information: The chatbot provides relevant and appropriate information/answer to people at each stage to make them closer to their goal. 3. Maxim of quantity: The chatbot responds in an informative way without adding too much information. 4. Resilience to failure: Chatbot seems able to find ways to respond appropriately even when it encounters situations or arguments it is not equipped to handle. 5. Understandability and politeness: The chatbot seems able to understand input and convey correct statements and answers without ambiguity and with acceptable manners. 6. Perceived conversational credibility: The chatbot responds in a credible and informative way without adding too much information. 7. Meet the neurodiverse needs: Chatbot seems able to meet needs and be used by users independently form their health conditions, well-being, age, etc. Expert Panel and Data Collection A panel of experts (two genetic counselors and two clinical geneticists) was provided with a link to the survey containing the questions. They independently evaluated the responses from ChatGPT 4 without discussing the questions or answers among themselves until after the survey submission. This approach ensured unbiased evaluation.