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14 results for “messaging data”
Cebulka (Polish dark web cryptomarket and image board) messages data
<h3><strong>General Information</strong></h3> <p>1. <strong>Title of Dataset</strong></p> <p>Cebulka (Polish dark web cryptomarket and image board) messages data.</p> <p>2. <strong>Data Collectors</strong></p> <p>Haitao Shi (The University of Edinburgh, UK); Patrycja Cheba (Jagiellonian University); Leszek Świeca (Kazimierz Wielki University in Bydgoszcz, Poland).</p> <p>3. <strong>Funding Information</strong></p> <p>The dataset is part of the research supported by the Polish National Science Centre (Narodowe Centrum Nauki) grant 2021/43/B/HS6/00710.</p> <p>Project title: “Rhizomatic networks, circulation of meanings and contents, and offline contexts of online drug trade” (2022-2025; PLN 956 620; funding institution: Polish National Science Centre [NCN], call: OPUS 22; Principal Investigator: Piotr Siuda [Kazimierz Wielki University in Bydgoszcz, Poland]).</p> <h3><strong>Data Collection Context</strong></h3> <p>4.<strong> Data Source</strong></p> <p>Polish dark web cryptomarket and image board called Cebulka (<a href="http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/index.php">http://cebulka7uxchnbpvmqapg5pfos4ngaxglsktzvha7a5rigndghvadeyd.onion/index.php</a>). </p> <p>5. <strong>Purpose</strong></p> <p>This dataset was developed within the abovementioned project. The project focuses on studying internet behavior concerning disruptive actions, particularly emphasizing the online narcotics market in Poland. The research seeks to (1) investigate how the open internet, including social media, is used in the drug trade; (2) outline the significance of darknet platforms in the distribution of drugs; and (3) explore the complex exchange of content related to the drug trade between the surface web and the darknet, along with understanding meanings constructed within the drug subculture.</p> <p>Within this context, Cebulka is identified as a critical digital venue in Poland’s dark web illicit substances scene. Besides serving as a marketplace, it plays a crucial role in shaping the narratives and discussions prevalent in the drug subculture. The dataset has proved to be a valuable tool for performing the analyses needed to achieve the project’s objectives.</p> <h3><strong>Data Content</strong></h3> <p>6. <strong>Data Description</strong></p> <p>The data was collected in three periods, i.e., in January 2023, June 2023, and January 2024.</p> <p>The dataset comprises a sample of messages posted on Cebulka from its inception until January 2024 (including all the messages with drug advertisements). These messages include the initial posts that start each thread and the subsequent posts (replies) within those threads. The dataset is organized into two directories. The “cebulka_adverts” directory contains posts related to drug advertisements (both advertisements and comments). In contrast, the “cebulka_community” directory holds a sample of posts from other parts of the cryptomarket, i.e., those not related directly to trading drugs but rather focusing on discussing illicit substances. The dataset consists of 16,842 posts.</p> <p>7. <strong>Data Cleaning, Processing, and Anonymization</strong></p> <p>The data has been cleaned and processed using regular expressions in Python. Additionally, all personal information was removed through regular expressions. The data has been hashed to exclude all identifiers related to instant messaging apps and email addresses. Furthermore, all usernames appearing in messages have been eliminated.</p> <p>8. <strong>File Formats and Variables/Fields</strong></p> <p>The dataset consists of the following files:</p> <ul> <li>Zipped .txt files (“cebulka_adverts.zip” and “cebulka_community.zip”) containing all messages. These files are organized into individual directories that mirror the folder structure found on Cebulka.</li> <li>Two .csv files that list all the messages, including file names and the content of each post. The first .csv lists messages from “cebulka_adverts.zip,” and the second .csv lists messages from “cebulka_community.zip.”</li> </ul> <h3><strong>Ethical Considerations</strong></h3> <p>9. <strong>Ethics Statement</strong></p> <p>A set of data handling policies aimed at ensuring safety and ethics has been outlined in the following paper:</p> <p>Harviainen, J.T., Haasio, A., Ruokolainen, T., Hassan, L., Siuda, P., Hamari, J. (2021). Information Protection in Dark Web Drug Markets Research [in:] Proceedings of the 54th Hawaii International Conference on System Sciences, HICSS 2021, Grand Hyatt Kauai, Hawaii, USA, 4-8 January 2021, Maui, Hawaii, (ed.) Tung X. Bui, Honolulu, HI, pp. 4673-4680.</p> <p>The primary safeguard was the early-stage hashing of usernames and identifiers from the messages, utilizing automated systems for irreversible hashing. Recognizing that automatic name removal might not catch all identifiers, the data underwent manual review to ensure compliance with research ethics and thorough anonymization.</p>
Hyperreal Talk (Polish clear web message board) messages data
<h3><strong>General Information</strong></h3> <p>1.<strong> Title of Dataset</strong></p> <p>Hyperreal Talk (Polish clear web message board) messages data.</p> <p>2. <strong>Data Collectors</strong></p> <p>Haitao Shi (The University of Edinburgh, UK); Leszek Świeca (Kazimierz Wielki University in Bydgoszcz, Poland).</p> <p>3. <strong>Funding Information</strong></p> <p>The dataset is part of the research supported by the Polish National Science Centre (Narodowe Centrum Nauki) grant 2021/43/B/HS6/00710.</p> <p>Project title: “Rhizomatic networks, circulation of meanings and contents, and offline contexts of online drug trade” (2022-2025; PLN 956 620; funding institution: Polish National Science Centre [NCN], call: OPUS 22; Principal Investigator: Piotr Siuda [Kazimierz Wielki University in Bydgoszcz, Poland]).</p> <h3><strong>Data Collection Context</strong></h3> <p>4.<strong> Data Source</strong></p> <p>Polish clear web message board called Hyperreal Talk (<a href="https://hyperreal.info/talk/">https://hyperreal.info/talk/</a>).</p> <p>5.<strong> Purpose</strong></p> <p>This dataset was developed within the abovementioned project. The project delves into internet dynamics within disruptive activities, specifically focusing on the online drug trade in Poland. It aims to (1) examine the utilization of the open internet, including social media, in the drug trade; (2) delineate the role of darknet environments in narcotics distribution; and (3) uncover the intricate flow of drug trade-related content and its meanings between the open web and the darknet, and how these meanings are shaped within the so-called drug subculture.</p> <p>The Hyperreal Talk forum emerges as a pivotal online space on the Polish internet, serving as a hub for discussions and the exchange of knowledge and experiences concerning drug use. It plays a crucial role in investigating the narratives and discourses that shape the drug subculture and the broader societal perceptions of drug consumption. The dataset has been instrumental in conducting analyses pertinent to the earlier project goals.</p> <p>6. <strong>Collection Method</strong></p> <p>The dataset was compiled using the Scrapy framework, a web crawling and scraping library for Python. This tool facilitated systematic content extraction from the targeted message board.</p> <p>7.<strong> Collection Date</strong></p> <p>The data was collected in two periods, i.e., in September 2023 and November 2023.</p> <h3><strong>Data Content</strong></h3> <p>8. <strong>Data Description</strong></p> <p>The dataset comprises all messages posted on the Polish-language Hyperreal Talk message board from its inception until November 2023. These messages include the initial posts that start each thread and the subsequent posts (replies) within those threads. The dataset is organized into two directories: “hyperreal” and “hyperreal_hidden.” The “hyperreal” directory contains accessible posts without needing to log in to Hyperreal Talk, while the “hyperreal_hidden” directory holds posts that can only be viewed by logged-in users. For each directory, a .txt file has been prepared detailing the structure of the message board folders from which the posts were extracted. The dataset includes 6,248,842 posts.</p> <p>9.<strong> Data Cleaning, Processing, and Anonymization</strong></p> <p>The data has been cleaned and processed using regular expressions in Python. Additionally, all personal information was removed through regular expressions. The data has been hashed to exclude all identifiers related to instant messaging apps and email addresses. Furthermore, all usernames appearing in messages have been eliminated.</p> <p>10. <strong>File Formats and Variables/Fields</strong></p> <p>The dataset consists of the following files:</p> <ul> <li>Zipped .txt files (hyperreal.zip) containing messages that are visible without logging into Hyperreal Talk. These files are organized into individual directories that mirror the folder structure found on the Hyperreal Talk message board.</li> <li>Zipped .txt files (hyperreal_hidden.zip) containing messages that are visible only after logging into Hyperreal Talk. Similar to the first type, these files are organized into directories corresponding to the website’s folder structure.</li> <li>A .csv file that lists all the messages, including file names and the content of each post.</li> </ul> <h3><strong>Accessibility and Usage</strong></h3> <p>11.<strong> Access Conditions</strong></p> <p>The data can be accessed without any restrictions.</p> <p>12. <strong>Related Documentation</strong></p> <p>Attached are .txt files detailing the tree of folders for “hyperreal.zip” and “hyperreal_hidden.zip.”</p> <p>Documentation on the Python regular expressions used for scraping, cleaning, processing, and anonymizing the data can be found on GitHub at the following URLs:</p> <ul> <li><a href="https://github.com/LeszekSwieca/Project_2021-43-B-HS6-00710">https://github.com/LeszekSwieca/Project_2021-43-B-HS6-00710</a></li> <li><a href="https://github.com/HaitaoShi/Scrapy_hyperreal">https://github.com/HaitaoShi/Scrapy_hyperreal</a>"</li> </ul> <h3><strong>Ethical Considerations</strong></h3> <p>13. <strong>Ethics Statement</strong></p> <p>A set of data handling policies aimed at ensuring safety and ethics has been outlined in the following paper:</p> <p>Harviainen, J.T., Haasio, A., Ruokolainen, T., Hassan, L., Siuda, P., Hamari, J. (2021). Information Protection in Dark Web Drug Markets Research [in:] Proceedings of the 54th Hawaii International Conference on System Sciences, HICSS 2021, Grand Hyatt Kauai, Hawaii, USA, 4-8 January 2021, Maui, Hawaii, (ed.) Tung X. Bui, Honolulu, HI, pp. 4673-4680.</p> <p>The primary safeguard was the early-stage hashing of usernames and identifiers from the messages, utilizing automated systems for irreversible hashing. Recognizing that scraping and automatic name removal might not catch all identifiers, the data underwent manual review to ensure compliance with research ethics and thorough anonymization.</p>
Dopek.eu (Polish clear web and dark web message board) messages data
<h3><strong>General Information</strong></h3> <p>1. <strong>Title of Dataset</strong></p> <p>Dopek.eu (Polish clear web and dark web message board) messages data.</p> <p>2. <strong>Data Collectors</strong></p> <p>Haitao Shi (The University of Edinburgh, UK); Leszek Świeca (Kazimierz Wielki University in Bydgoszcz, Poland).</p> <p>3. <strong>Funding Information</strong></p> <p>The dataset is part of the research supported by the Polish National Science Centre (Narodowe Centrum Nauki) grant 2021/43/B/HS6/00710.</p> <p>Project title: “Rhizomatic networks, circulation of meanings and contents, and offline contexts of online drug trade” (2022-2025; PLN 956 620; funding institution: Polish National Science Centre [NCN], call: OPUS 22; Principal Investigator: Piotr Siuda [Kazimierz Wielki University in Bydgoszcz, Poland]).</p> <h3><strong>Data Collection Context</strong></h3> <p>4. <strong>Data Source</strong></p> <p>Clear web and dark web message board called dopek.eu (<a href="https://dopek.eu/">https://dopek.eu/</a>). </p> <p>5.<strong> Purpose</strong></p> <p>This dataset was developed within the abovementioned project. The project delves into internet dynamics within disruptive activities, specifically focusing on the online drug trade in Poland. It aims to (1) examine the utilization of the open internet, including social media, in the drug trade; (2) delineate the role of darknet environments in narcotics distribution; and (3) uncover the intricate flow of drug trade-related content and its meanings between the open web and the darknet, and how these meanings are shaped within the so-called drug subculture.</p> <p>The dopek.eu forum emerges as a pivotal online space on the Polish internet, serving as a hub for trading, discussions, and the exchange of knowledge and experiences concerning the use of the so-called new psychoactive substances (designer drugs). The dataset has been instrumental in conducting analyses pertinent to the earlier project goals.</p> <p>6.<strong> Collection Method</strong></p> <p>The dataset was compiled using the Scrapy framework, a web crawling and scraping library for Python. This tool facilitated systematic content extraction from the targeted message board.</p> <p>7.<strong> Collection Date</strong></p> <p>The data was collected in October 2023.</p> <h3><strong>Data Content</strong></h3> <p>8.<strong> Data Description</strong></p> <p>The dataset comprises all messages posted on dopek.eu from its inception until October 2023. These messages include the initial posts that start each thread and the subsequent posts (replies) within those threads. A .txt file has been prepared detailing the structure of the message board folders from which the posts were extracted. The dataset includes 171,121 posts.</p> <p>9.<strong> Data Cleaning, Processing, and Anonymization</strong></p> <p>The data has been cleaned and processed using regular expressions in Python. Additionally, all personal information was removed through regular expressions. The data has been hashed to exclude all identifiers related to instant messaging apps and email addresses. Furthermore, all usernames appearing in messages have been eliminated.</p> <p>10. <strong>File Formats and Variables/Fields</strong></p> <p>The dataset consists of the following types of files:</p> <ul> <li>Zipped .txt files (dopek.zip) containing all messages (posts).</li> <li>A .csv file that lists all the messages, including file names and the content of each post.</li> </ul> <h3><strong>Accessibility and Usage</strong></h3> <p><strong>11. Access Conditions</strong></p> <p>The data can be accessed without any restrictions.</p> <p><strong>12. Related Documentation</strong></p> <p>Attached are .txt files detailing the tree of folders for “dopek.zip”.</p> <h3><strong>Ethical Considerations</strong></h3> <p><strong>13. Ethics Statement</strong></p> <p>A set of data handling policies aimed at ensuring safety and ethics has been outlined in the following paper:</p> <p>Harviainen, J.T., Haasio, A., Ruokolainen, T., Hassan, L., Siuda, P., Hamari, J. (2021). Information Protection in Dark Web Drug Markets Research [in:] Proceedings of the 54th Hawaii International Conference on System Sciences, HICSS 2021, Grand Hyatt Kauai, Hawaii, USA, 4-8 January 2021, Maui, Hawaii, (ed.) Tung X. Bui, Honolulu, HI, pp. 4673-4680.</p> <p>The primary safeguard was the early-stage hashing of usernames and identifiers from the posts, utilizing automated systems for irreversible hashing. Recognizing that scraping and automatic name removal might not catch all identifiers, the data underwent manual review to ensure compliance with research ethics and thorough anonymization.</p>
Supporting Data: Do spatial climate messages increase pro-environmental engagement? Evidence from a survey experiment on public transport
<p>Additional material for the paper:</p> <p>Victor von Loessl, Eva Weingärtner & Sonja Zitzelsberger (2022) Do spatial climate messages increase pro-environmental engagement? Evidence from a survey experiment on public transport, Journal of Environmental Economics and Policy, DOI: 10.1080/21606544.2022.2097960</p>
Post-op Crowd Sourcing Health Data Via Text-messaging
ClinicalTrials.gov study NCT03532256. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: Mitigating collision-caused bird mortality through message framing: Insights from residents' intentions for bird-safe windows
Open the record for dataset details and reuse information.
Supplementary material 1 from: Zilletti B, Capdevila-Argüelles L (2014) Halting Biological Invasions in Europe: from Data to Decisions. A message from NEOBIOTA 2012. NeoBiota 21: 1-5. https://doi.org/10.3897/neobiota.21.7289
Time to act! Biological invasions need a strong European legal framework urgently!: Explanation note: Resolution adopted by the participants of the "7th European Conference on Biological Invasions" of the European Working Group on Biological Invasions - NEOBIOTA.
"The main message is that sustainability would help" – Reflections on takeaway messages of climate change data visualizations.
<p>As part of a larger project investigating expert opinions on public climate change communication and the role of data visualizations1, we conducted semi-structured interviews with 17 experts in the fields of climate change, science communication, or data visualization. We also interviewed six members of the general public with no professional background in either of these areas, who we refer to as lay participants. In the following, lay participants are identified as L-1 to L-6 and expert participants as E-1 to E-17.</p> <p>We used two example visualizations from online news sources (if preferred, translated to German) as a discussion basis and asked participants about the main takeaway message the visualization author wanted to convey. Example visualization 1 was also shown to 17 experts, resulting in a total of 23 formulated takeaway messages. Example visualization 2 was shown to all six lay participants and to seven experts, resulting in a total of 13 messages. For both example visualizations and an overall count of 36 formulated takeaway messages for thematic analysis, we have observed variations in the included contents and the length/abstraction of messages, as well as in the sensemaking process itself among the two participant groups, but also between lay and expert participants. This document provides an overview of those 36 takeaway messages formulated by the study participants.</p>
Data from: Identifying and overcoming breastfeeding challenges using two-way short message service, a randomized controlled trial
<p>Breastfeeding challenges, such as pain and perception of insufficient milk supply, prevent many women from exclusively breastfeeding their babies for the six-month duration recommended by the World Health Organization. Mobile health strategies may help women identify and overcome breastfeeding challenges. This study evaluated a two-way short message service (SMS) intervention that delivered breastfeeding messages to women in Kiambu County, Kenya, and its impact on rates of exclusive breastfeeding and breastfeeding behavior at 1, 4, and 6 months post-delivery. Women in the control group received a general package of messages containing postnatal information; women in the intervention group received these messages, plus messages related to breastfeeding and surveys which offered more information about breastfeeding challenges. Both groups of women had access to a help desk agent who responded to questions via SMS.</p> <p>There was no significant difference in the rate of exclusive breastfeeding between the intervention and control at any time point (p=.861). Women in the intervention group were more likely to report experiencing breastfeeding challenges such as pain and perceived insufficient milk supply than those in the control group (32% vs. 24%, p=0.00). Of the women in the intervention group who reported experiencing breastfeeding challenges, 70% of them found a resolution to their challenge, and 23% of them resolved their challenges using SMS. SMS may help women in low-resource settings to identify and overcome their breastfeeding challenges.</p>
Data from: Changing culture through pro-environmental messaging on digital signs: A longitudinal field study
Open the record for dataset details and reuse information.
Data from: Identifying and overcoming breastfeeding challenges using two-way short message service, a randomized controlled trial
Open the record for dataset details and reuse information.
Message Communicating Latest Data on COVID-19 Transmission in Patient's Area
ClinicalTrials.gov study NCT05621226. IPD Sharing: NO. Countries: 1. Publications: 0.
Data Donation with Dona: De-identified Messaging Data (WhatsApp and Facebook) and Evaluation Responses
<p><strong>General information</strong></p> <p>The dataset contains de-identified messaging meta-data from 78 WhatsApp and 7 Facebook data donations. The dataset was collected in an online study using the data donation platform Dona. After donating their messaging data, the study participants viewed visual summaries of their messaging data and evaluated this visual feedback. The responses to the evaluation questions and the sociodemographic data of the participants are also included in the dataset. </p> <p>The data was collected from August 2022 to June 2024. </p> <p>For more information on Dona, the associated publications and updates, please visit https://mbp-lab.github.io/dona-blog/. </p> <p><strong>File description<br></strong></p> <ol> <li><code>donation_table.csv </code>- contains general information about the donations including <ul> <li><em>donation_id</em>: donation identifier</li> <li><em>donor_id</em>: the ID of the donor to distinguish the messages sent by them from those sent by contacts</li> <li><em>source</em>: the messaging platform from which the data is donated (WhatsApp or Facebook)</li> <li><em>external_id</em>: ID used to connect messaging data with the survey data</li> </ul> </li> <li> <code>messages_table.csv</code> - contains the donated messages including<br> <ul> <li><em>conversation_id</em>: chat identifier</li> <li><em>sender_id</em>: sender identifier</li> <li><em>datetime</em>: time of the message, UNIX time for Facebook and device time for WhatsApp</li> <li><em>word_count</em>: word count of the messages achieved by splitting the text based on whitespace</li> <li><em>donation_id</em>: donation identifier (also listed in <code>donation_table.csv</code>)</li> </ul> </li> <li><code>messages_filtered_table.csv </code>- same structure as <code>messages_table.csv</code> except that chats with no considerable interactions were removed. This was defined as chats where donor's word count contribution was less than 10% or more than 90%. </li> <li><code>survey.xlsx </code>→ contains survey responses of the participants.</li> <li><code>survey_table_coding.xlsx </code>→ contains the mapping between the column names in <code>survey.xlsx </code>and their meaning, including the original survey questions and response options. Different sheets of the Excel file detail the survey questions and responses in one of the study languages (English, German, Armenian). </li> </ol> <p> </p>
Hateful Messages: A Conversational Data Set of Hate Speech produced by Adolescents on Discord
<p>With the rise of social media, a rise of hateful content can be observed. Even though the understanding and definitions of hate speech varies, platforms, communities, and legislature all acknowledge the problem. Therefore, adolescents are a new and active group of social media users. The majority of adolescents experience or witness online hate speech. Research in the field of automated hate speech classification has been on the rise and focuses on aspects such as bias, generalizability, and performance. To increase generalizability and performance, it is important to understand biases within the data. This research addresses the bias of youth language within hate speech classification and contributes by providing a modern and anonymized hate speech youth language data set consisting of 88.395 annotated chat messages. The data set consists of publicly available online messages from the chat platform Discord. ~6,42\% of the messages were classified by a self-developed annotation schema as hate speech. For 35.553 messages, the user profiles provided age annotations setting the average author age to under 20 years old.</p>
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