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High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users by WhatsApp and the action taken by it. The data compiled is based on the monthly transparency reports published by WhatsApp in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received include Account Support, Safety, Ban Appeal, Other Support and Product Support. The action taken includes the accounts actioned on the basis of grievances received.

Notes:

1. ‘Accounts Actioned’ denotes reports where WhatsApp took remedial action based on the report. Taking action denotes either banning an account or a previously banned account being restored as a result of the complaint.
2. ‘Other’ support pertains to requests which are not consistently classifiable
3. ‘N/A’ denotes grievance topics where it’s generally not applicable to action an account. In some rare cases, tickets may be later reassigned to another topic, and an action could arise from the re-assignment.

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MultiSocial is a dataset (described in a paper) for multilingual (22 languages) machine-generated text detection benchmark in social-media domain (5 platforms). It contains 472,097 texts, of which about 58k are human-written and approximately the same amount is generated by each of 7 multilingual large language models by using 3 iterations of paraphrasing. The dataset has been anonymized to minimize amount of sensitive data by hiding email addresses, usernames, and phone numbers.

If you use this dataset in any publication, project, tool or in any other form, please, cite the a paper.

Disclaimer

Due to data source (described below), the dataset may contain harmful, disinformation, or offensive content. Based on a multilingual toxicity detector, about 8% of the text samples are probably toxic (from 5% in WhatsApp to 10% in Twitter). Although we have used data sources of older date (lower probability to include machine-generated texts), the labeling (of human-written text) might not be 100% accurate. The anonymization procedure might not successfully hiden all the sensitive/personal content; thus, use the data cautiously (if feeling affected by such content, report the found issues in this regard to dpo[at]kinit.sk). The intended use if for non-commercial research purpose only.

Data Source

The human-written part consists of a pseudo-randomly selected subset of social media posts from 6 publicly available datasets:

1. Telegram data originated in Pushshift Telegram, containing 317M messages (Baumgartner et al., 2020). It contains messages from 27k+ channels. The collection started with a set of right-wing extremist and cryptocurrency channels (about 300 in total) and was expanded based on occurrence of forwarded messages from other channels. In the end, it thus contains a wide variety of topics and societal movements reflecting the data collection time.

2. Twitter data originated in CLEF2022-CheckThat! Task 1, containing 34k tweets on COVID-19 and politics (Nakov et al., 2022, combined with Sentiment140, containing 1.6M tweets on various topics (Go et al., 2009).

3. Gab data originated in the dataset containing 22M posts from Gab social network. The authors of the dataset (Zannettou et al., 2018) found out that “Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls.” They also found out that hate speech is much more prevalent there compared to Twitter, but lower than 4chan's Politically Incorrect board.

4. Discord data originated in Discord-Data, containing 51M messages. This is a long-context, anonymized, clean, multi-turn and single-turn conversational dataset based on Discord data scraped from a large variety of servers, big and small. According to the dataset authors, it contains around 0.1% of potentially toxic comments (based on the applied heuristic/classifier).

5. WhatsApp data originated in whatsapp-public-groups, containing 300k messages (Garimella & Tyson, 2018). The public dataset contains the anonymised data, collected for around 5 months from around 178 groups. Original messages were made available to us on request to dataset authors for research purposes.

From these datasets, we have pseudo-randomly sampled up to 1300 texts (up to 300 for test split and the remaining up to 1000 for train split if available) for each of the selected 22 languages (using a combination of automated approaches to detect the language) and platform. This process resulted in 61,592 human-written texts, which were further filtered out based on occurrence of some characters or their length, resulting in about 58k human-written texts.

The machine-generated part contains texts generated by 7 LLMs (Aya-101, Gemini-1.0-pro, GPT-3.5-Turbo-0125, Mistral-7B-Instruct-v0.2, opt-iml-max-30b, v5-Eagle-7B-HF, vicuna-13b). All these models were self-hosted except for GPT and Gemini, where we used the publicly available APIs. We generated the texts using 3 paraphrases of the original human-written data and then preprocessed the generated texts (filtered out cases when the generation obviously failed).

The dataset has the following fields:

• 'text' - a text sample,

• 'label' - 0 for human-written text, 1 for machine-generated text,

• 'multi_label' - a string representing a large language model that generated the text or the string "human" representing a human-written text,

• 'split' - a string identifying train or test split of the dataset for the purpose of training and evaluation respectively,

• 'language' - the ISO 639-1 language code identifying the detected language of the given text,

• 'length' - word count of the given text,

• 'source' - a string identifying the source dataset / platform of the given text,

• 'potential_noise' - 0 for text without identified noise, 1 for text with potential noise.

ToDo Statistics (under construction)

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The What's New, Switzerland? Corpus is a dataset of 72 authentic WhatsApp chats between 118 French-speaking users in Switzerland, collected in the framework of the "Evolving Language" NCCR. Chats were donated by users between August and October 2022. The data have been de-identified using a partly automated and partly manual workflow. Each chat is provided in two versions: an XML-TEI version (which includes extensive metadata about chats, users, and messages) and a plain text version. The dataset is available on demand for research purposes, under a restricted license contract.

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Two datasets are included in this repository: claim matching and claim detection datasets. The collections contain data in 5 languages: Bengali, English, Hindi, Malayalam and Tamil.

The "claim detection" dataset contains textual claims from social media and fact-checking websites annotated for the "fact-check worthiness" of the claims in each message. Data points have one of the three labels of "Yes" (text contains one or more check-worthy claims), "No" and "Probably".

The "claim matching" dataset is a curated collection of pairs of textual claims from social media and fact-checking websites for the purpose of automatic and multilingual claim matching. Pairs of data have one of the four labels of "Very Similar", "Somewhat Similar", "Somewhat Dissimilar" and "Very Dissimilar".

All personally identifiable information (PII) including phone numbers, email addresses, license plate numbers and addresses have been replaced with general tags (e.g. , , etc) to protect user anonymity. A detailed explanation on the curation and annotation process is provided in our ACL 2021 paper: Kazemi, A.; Garimella, K.; Gaffney, D.; and Hale, S. A. 2021. Claim Matching Beyond English to Scale Global Fact-Checking. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, ACL 2021.

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The dataset contains year, month and payment application-wise UPI Apps Transaction Statistics like Customer Initiated Transactions, B2C Transactions, B2B Transactions and On-us Transactions Note: 1) Unified Payments Interface(UPI) is an instant real-time payment system developed by National Payments Corporation of India. The interface facilitates inter-bank peer-to-peer and person-to-merchant transactions 2) From January 2021 onwards, ‚On-us Transactions‚ in UPI that are not processed and settled through the UPI Central System is shown under ‚ On-us Transactions column 3) Apps which has volume less than 10,000 is included under‚ Other Apps. 4) App volume in table is basis the Payer App logic, i.e the financial transaction is attributed to the PSP in UPI on the Payer's side. 5) BHIM Volume is inclusive of *99# volume. 6) For WhatsApp, Maximum registered user base of hundred (100) million in UPI

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The Behavioural Change Technique (BCT) Taxonomy provides a vital resource for systematically defining, categorising and testing innovations in behaviour change strategy. The BCTs are 93 distinct strategies that are clearly defined and categorised. BCTs enable consistent definition and distinction for component parts in complex behaviour change interventions. The Patient Engagement Lab sought to test and compare the effects of alternative BCTs on mediating outcomes in large-scale mhealth programmes. Relevant mediating outcomes include sharing text messages with others, reading WhatsApp messages, responding to two-way message prompts and self-reported engagement in desired health behaviours (such as clinic attendance and adherence to contraceptive or other treatment regimens). The WhatsApp BCT Prototype Testing Guide was developed to support more rapid integration and testing of BCTs on a national mHealth platform in South Africa. Implementing BCTs on internet protocol messaging programmes can, however, be a complex and time consuming task. The prototypes offered here were developed by an experienced South African digital content designer and a South African service user from the Patient Engagement Lab. The team assessed the feasibility of implementing each BCT using WhatsAPP API integration and where BCTs were found to be feasible, scripts were developed showing how they could be implemented and then revised with service user input. The BCT prototypes provided in the WhatsApp BCT Prototype Testing Guide focus on two key focus areas in mhealth work in low- and middle-income countries: reproductive health / family planning messaging and HIV testing. They are designed to be implemented using WhatsApp API integration specifically in South Africa; most features are, however, generalisable to other Internet Protocol messaging applications or platforms (such as Facebook Messenger, Signal or Telegram). Indeed, implementation via WhatsApp may now be the most challenging because WhatsApp since changed their Terms and Conditions. Alongside the BCT prototypes, the WhatsApp BCT Prototype Testing Guide links BCTs to terminology used in different fields (e.g. ‘nudges’ tested in behavioural economics trials); and offers a table systematically matching BCTs to mediating outcomes that are observable on WhatsApp (and other internet protocol) messaging platforms. These three datasets were developed together to support more efficient testing and optimisation of our messaging platforms. We hope these resources can be of use to others seeking to develop and iteratively improve their mhealth platforms.