This repository contains two datasets used in the study exploring the impact of Generative AI, specifically ChatGPT, on the public sector workforce in the United States. The datasets provide detailed information on the core tasks of public sector occupations and their estimated performance metrics, including potential for automation and augmentation by ChatGPT. These estimations are generated by OpenAI’s GPT-4 model (GPT-4-1106-preview) through OpenAI API.

The rapid advancements in generative AI models present new opportunities in the education sector. However, it is imperative to acknowledge and address the potential risks and concerns that may arise with their use. We collected Twitter data to identify key concerns related to the use of ChatGPT in education. This dataset is used to support the study "ChatGPT in education: A discourse analysis of worries and concerns on social media."

In this study, we particularly explored two research questions. RQ1 (Concerns): What are the key concerns that Twitter users perceive with using ChatGPT in education? RQ2 (Accounts): Which accounts are implicated in the discussion of these concerns? In summary, our study underscores the importance of responsible and ethical use of AI in education and highlights the need for collaboration among stakeholders to regulate AI policy.

Discover how AI code interpreters are revolutionizing data visualization, reducing chart creation time from 20 to 5 minutes while simplifying complex statistical analysis.

Monthly dataset tracking topic frequency, keyword volume, and conversation patterns across ChatGPT discussions. Data is normalized on a 0 to 100 scale for easy comparison. Aggregates millions of AI interactions to reveal emerging trends, user interests, and discussion momentum across technology, finance, health, education, and business categories.

Don't forget to hit the UpVote🙏🙏

The DataSet consists of user reviews of ChatGPT, including Textual Feedback, Ratings, and Review Dates. The Reviews Range from brief comments to more detailed feedback by covering a wide range of user sentiments. The ratings are on a scale of 1 to 5, representing varying levels of Satisfaction. The dataset spans multiple months, providing a temporal dimension for analysis. Each review is accompanied by a timestamp, allowing for Time-Series analysis of sentiment trends.

1. Review Id:

• Description: A unique identifier for each review.
• Data Type: String (UUID format).

2. Review:

• Description: The text of the user review. This provides qualitative feedback about the app.
• Data Type: String

3. Ratings:

• Description: User-provided ratings on a scale (likely 1-5) to indicate their level of satisfaction.
• Data Type: Integer
• Range: 1 (lowest) to 5 (highest)

4. Review Date:

• Description: The timestamp when the review was submitted.
• Data Type: Date_Time
• Format: MM/DD/YYYY HH:MM

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.

Here you can find about 50K comments on Reddit website regarding ChatGPT . The comments are gathered from Reddit's Posts from 4 subreddits.

The data includes comment_id, comment_parent_id, comment_body and subreddit

• comment_id : the comment's id
• comment_parent_id: the comment's id which the current comment is replied to.
• comment_body: the comment
• subreddit: the community/subreddit name of the comment

The Date and other information related to comments will be added in the next version. This dataset is useful to get insight about the public take on ChatGPT and also for text analysis, text visualizations, Inline Question Answering, Text Summarization, NER and other tasks like clustering and so on.

Please note that this dataset is not cleaned or preprocessed so if you want to get your hands dirty with data, it's a good practice to level up your skills in data cleaning too :)

And please don't forget to UPVOTE it in case you find it useful and enjoy it.

Analysis of 13,252 publicly shared ChatGPT conversations by WebFX to uncover usage statistics - prompt length, message count, question vs command distribution, use-case categories.

ObjectiveTo assess the competence of students and academic staff to use generative artificial intelligence (GenAI) as a tool in epidemiological data analyses in a randomised controlled trial (RCT).MethodsWe invited postgraduate students and academic staff at the Swiss Tropical and Public Health Institute to the RCT. Participants were randomized to analyse a simulated cross-sectional dataset using ChatGPT’s code interpreter (integrated analysis arm) vs. a statistical software (R/Stata) with ChatGPT as a support tool (distributed analysis arm). The primary outcome was the trial task score (out of 17, using an assessment rubric). Secondary outcome was the time to complete the task.ResultsWe invited 338 and randomized 31 participants equally to the two study arms and 30 participants submitted results. Overall, there was no statistically significant difference in mean task scores between the distributed analysis arm (8.5, ±4.6) and the integrated analysis arm (9.4, ±3.8), with a mean difference of 0.93 (p = 0.55). Mean task completion time was significantly shorter in the integrated analysis arm compared to the distributed analysis arm.ConclusionWhile ChatGPT offers advantages, its effective use requires a careful balance of GenAI capabilities and human expertise.

This repository contains the links to all the related experiments that I run related to my article titled Using "LLM for finding security vulnerabilities."

Dataset Description

This dataset consists of daily-updated user reviews and ratings for the ChatGPT Android App. The dataset includes several key attributes that capture various aspects of the reviews, providing insights into user experiences and feedback over time.

Columns Explanation

• userName: The display name of the user who posted the review.
• content: The text content of the review. This column contains the actual review text written by the user. It includes user opinions, feedback, and detailed descriptions of their experiences with the ChatGPT app.
• score: The rating given by the user, typically ranging from 1 to 5. This column captures the numerical rating provided by the user. Higher scores indicate better experiences, while lower scores indicate dissatisfaction.
• thumbsUpCount: The number of thumbs up (likes) the review received. This column shows how many other users found the review helpful or agreed with the sentiments expressed. It serves as a measure of the review's relevancy and impact.
• at: The timestamp of when the review was posted. This column includes the date and time when the review was submitted. It is crucial for tracking the temporal distribution of reviews and analyzing trends over time.

Collection Methods

• Data Source: The data is collected from user reviews submitted through the ChatGPT Android App's review section on the Google Play Store.
• Frequency: The dataset is updated daily to capture the most recent user feedback and ratings.
• Automation: An automated script is used to scrape and compile the reviews, ensuring that the dataset is current and comprehensive.
• Data Cleaning: Basic preprocessing is performed to ensure data quality, such as removing duplicates and handling missing values.

Artificial Intelligence (AI) applications are expected to promote government service delivery and quality, more efficient handling of cases, and bias reduction in decision-making. One potential benefit of the AI tool ChatGPT is that it may support governments in the anonymization of data. However, it is not clear whether ChatGPT is appropriate to support data anonymization for public organizations. Hence, this study examines the possibilities, risks, and ethical implications for government organizations to employ ChatGPT in the anonymization of personal data. We use a case study approach, combining informal conversations, formal interviews, a literature review, document analysis and experiments to conduct a three-step study. First, we describe the technology behind ChatGPT and its operation. Second, experiments with three types of data (fake data, original literature and modified literature) show that ChatGPT exhibits strong performance in anonymizing these three types of texts. Third, an overview of significant risks and ethical issues related to ChatGPT and its use for anonymization within a specific government organization was generated, including themes such as privacy, responsibility, transparency, bias, human intervention, and sustainability. One significant risk in the current form of ChatGPT is a privacy risk, as inputs are stored and forwarded to OpenAI and potentially other parties. This is unacceptable if texts containing personal data are anonymized with ChatGPT. We discuss several potential solutions to address these risks and ethical issues. This study contributes to the scarce scientific literature on the potential value of employing ChatGPT for personal data anonymization in government. In addition, this study has practical value for civil servants who face the challenges of data anonymization in practice including resource-intensive and costly processes.

This dataset corresponds to the study carried out to analyse 10 bibliographic references of 10 Spanish authors in the field of Information Sciences requested to the ChatGPT chatbot.

The file "Bibliographic_references_ analysis" contains the 10 references returned by ChatGPT for each of the 10 authors (a total of 100 references), together with the variables analysed to check their authenticity.

The "Keywords_analysis" file contains the normalisation carried out on the words considered to be key words extracted from the titles of the works, according to which a word cloud showing the frequency of occurrence could be drawn up.

The data is scrapped using the Youtube API.

Index

videoId: A unique video ID of the Youtube Video. publishedAt: Date of upload of the video. channelID: A unique channel ID of the Youtube Channel. title: The title of the youtube video. channelTitle: The name of the channel. channelType: The Youtube Category ID of the Channel Type.

This repository contains the dataset and code used in the study titled “Academic Discourse on ChatGPT in Social Sciences: A Topic Modeling and Sentiment Analysis of Research Article Abstracts.” The study explores how social science scholars frame and evaluate ChatGPT by analyzing 1,227 SSCI-indexed abstracts using Latent Dirichlet Allocation (LDA) topic modeling and lexicon-based sentiment analysis. The data include the collected abstracts (with metadata), while the code files provide the full analytical pipeline in Python and R, covering preprocessing, topic modeling, sentiment scoring using the NRC Emotion Lexicon, and visualization scripts. This repository supports transparency, reproducibility, and reuse of the study’s computational methods and underlying materials.

This is the raw data that is used in the publication: ChatGPT as an education and learning tool for engineering, technology and general studies: performance analysis of ChatGPT 3.0 on CSE, GATE and JEE examinations of India.

ChatGPT has forever changed the way that many industries operate. Much of the focus of Artificial Intelligence (AI) has been on their ability to generate text. However, it is likely that their ability to generate computer codes and scripts will also have a major impact. We demonstrate the use of ChatGPT to generate Python scripts to perform hydrological analyses and highlight the opportunities, limitations and risks that AI poses in the hydrological sciences.

Here, we provide four worked examples of the use of ChatGPT to generate scripts to conduct hydrological analyses. We also provide a full list of the libraries available to the ChatGPT Advanced Data Analysis plugin (only available in the paid version). These files relate to a manuscript that is to be submitted to Hydrological Processes. The authors of the manuscript are Dylan J. Irvine, Landon J.S. Halloran and Philip Brunner.

If you find these examples useful and/or use them, we would appreciate if you could cite the associated publication in Hydrological Processes. Details to be made available upon final publication.

The research article discusses how the ethical issues when using generative conversational artificial intelligence systems about ChatGPT. Establishing an IT/IS approach to spread an ethical review of the new technologies aboput ChatGPT are intended for a better systematic review about the advantages and possible problems about ChatGPT and AI per say. This approach integrates ethical issues identified through proactive techniques. Ethical issues in new ICT applications, including ethics, ethical impact assessment and specific aspects of AI. Used to analyze the human text generation and interaction capabilities of ChatGPT. Also, the resarch analysis shows that ChatGPT can provide high levels of social and ethical benefits. But so does raises serious ethical concerns about social justice, individual autonomy, cultural identity, and environmental issues. Mental problems is one of the key issues with high impact include accountability, inclusion, social cohesion, autonomy, security, prejudice, responsibility and environmental impact. Although the current discussion focuses only on specific issues. This review consistently highlights a broader and more balanced range of ethical issues that, in the author's view, require attention. These findings are consistent with emerging research and industry priorities regarding the generative ethics of artificial intelligence. This includes the need to engage a variety of stakeholders and consider benefits and risks holistically. Participates in application development and multi-level policy interventions to achieve positive outcomes. Typical, The analysis shows that using established ethical engineering methods can lead to rigorous measurements. A comprehensive framework to guide discussion and action on new, impactful technologies such as ChatGPT. This article proposes to maintain this broad and balanced ethical perspective as use cases are developed to realize the benefits.

The dataset YouTube Comments about ChatGPT in Indonesian, obtained by web scraping technique on the video page "ChatGPT dan Masa Depan Pekerjaan Kita". contains 1249 data consisting of Comment attributes.

This if the data we used for our analysis