The dataset consists of various columns containing information related to tweets posted on Twitter. Each row in the dataset represents a single tweet. Here's an explanation of the columns in the dataset from a third-person perspective:

Tweet: This column contains the actual text content of the tweet. It includes the message that the user posted on Twitter. Tweets can vary in length from a few characters to the maximum allowed by Twitter.

Sentiment: This column indicates the sentiment or emotional tone of the tweet. Sentiment can be classified into categories such as positive, negative, or neutral. It reflects the overall opinion or attitude expressed in the tweet.

Username: This column contains the username of the Twitter account that posted the tweet. Each Twitter user has a unique username that identifies their account.

Timestamp: This column contains the timestamp indicating when the tweet was posted. It includes information about the date and time when the tweet was published on Twitter.

Retweets: This column represents the number of times the tweet has been retweeted by other Twitter users. A retweet is when a user shares another user's tweet with their followers.

Likes: This column indicates the number of likes or favorites received by the tweet. Users can express their appreciation for a tweet by liking it.

Hashtags: This column contains any hashtags included in the tweet. Hashtags are keywords or phrases preceded by the "#" symbol, used to categorize or label tweets and make them more discoverable.

Mentions: This column includes any Twitter usernames mentioned in the tweet. Mentions are when a user tags another user in their tweet by including their username preceded by the "@" symbol.

Location: This column provides information about the location associated with the tweet. It may include details such as the city, state, country, or geographical coordinates from which the tweet was posted, if available.

Source: This column specifies the source or platform used to post the tweet. It indicates whether the tweet was posted from the Twitter website, a mobile app, or a third-party application.

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

eBackgroundThe Digital Humanities 2016 conference is taking/took place in Kraków, Poland, between Sunday 11 July and Saturday 16 July 2016. #DH2016 is/was the conference official hashtag.What This Output IsThis is an Excel spreadsheet file containing three sheets containing a total of 3478 Tweets publicly published with the hashtag #DH2016. The archive starts with a Tweet published on Sunday July 10 2016 00:03:41 +0000 and finishes with a Tweet published on Tuesday July 12 2016 23:55:47 +0000.The original collection has been organised into conference days; one sheet per day (GMT and Central European Times included). A breakdown of Tweets per day:Sunday 10 July 2016: 179 TweetsMonday 11 July 2016: 981 TweetsTuesday 12 July 2016: 2318 Tweets Methodology and LimitationsThe Tweets contained in this file were collected by Ernesto Priego using Martin Hawksey's TAGS 6.0. Only users with at least 1 follower were included in the archive. Retweets have been included (Retweets count as Tweets). The collection spreadsheet was customised to reflect the time zone and geographical location of the conference.The profile_image_url and entities_str metadata were removed before public sharing in this archive. Please bear in mind that the conference hashtag has been spammed so some Tweets colllected may be from spam accounts. Some automated refining has been performed to remove Tweets not related to the conference but the data is likely to require further refining and deduplication. Both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that this file contains each and every Tweet tagged with #dh2016 during the indicated period, and the dataset is shared for archival, comparative and indicative educational research purposes only.Only content from public accounts is included and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.Each Tweet and its contents were published openly on the Web with the queried hashtag and are responsibility of the original authors.No private personal information is shared in this dataset. The collection and sharing of this dataset is enabled and allowed by Twitter's Privacy Policy. The sharing of this dataset complies with Twitter's Developer Rules of the Road. This dataset is shared to archive, document and encourage open educational research into scholarly activity on Twitter. Other ConsiderationsTweets published publicly by scholars during academic conferences are often tagged (labeled) with a hashtag dedicated to the conference in question.The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (Tweets in this case). A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting public professional networks that tend to observe scholarly practices and accepted modes of social and professional behaviour. In general terms it can be argued that scholarly Twitter users willingly and consciously tag their public Tweets with a conference hashtag as a means to network and to promote, report from, reflect on, comment on and generally contribute publicly to the scholarly conversation around conferences. As Twitter users, conference Twitter hashtag contributors have agreed to Twitter's Privacy and data sharing policies. Professional associations like the Modern Language Association recognise Tweets as citeable scholarly outputs. Archiving scholarly Tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection.Beyond individual tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. To date, collecting in real time is the only relatively accurate method to archive tweets at a small scale. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time.The CC-BY license has been applied to the output in the repository as a curated dataset. Authorial/curatorial/collection work has been performed on the file in order to make it available as part of the scholarly record. The data contained in the deposited file is otherwise freely available elsewhere through different methods and anyone not wishing to attribute the data to the creator of this output is needless to say free to do their own collection and clean their own data.

This dataset contains 70,427 cross-linked Twitter-GHTorrent user pairs identified as likely belonging to the same users. The dataset accompanies our research paper (PDF preprint here):

@inproceedings{fang2020tweet,
 author = {Fang, Hongbo and Klug, Daniel and Lamba, Hemank and Herbsleb, James and Vasilescu, Bogdan},
 title = {Need for Tweet: How Open Source Developers Talk About Their GitHub Work on Twitter},
 booktitle = {International Conference on Mining Software Repositories (MSR)},
 year = {2020},
 pages = {to appear},
 publisher = {ACM},
}

The data cannot be used for any purpose other than conducting research.

Due to privacy concerns, we only release the user IDs in Twitter and GHTorrent, respectively. We expect that users of this dataset will be able to collect other data using the Twitter API and GHTorrent, as needed. Please see below for an example.

To query the Twitter API for a given user_id, you can:

• Apply for Twitter developer account here.

• Create an APP with your Twitter developer account, and create an "API key" and "API secret key".

• Obtain an access token. Given the previous API keys, run:

  curl -u "

  The response looks like this: {"token_type":"bearer","access_token":"<...>"}

  Copy the "access_token".

• Given the previous access token, run:

  curl --request GET --url "https://api.twitter.com/1.1/users/show.json?user_id=

The GHTorrent user ids map to the users table in the MySQL version of GHTorrent. To use GHTorrent, please follow instructions on the GHTorrent website.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1041505%2F0625876b77e55a56422bb5a37d881e0d%2Fawdasdw.jpg?generation=1595666545033847&alt=media" alt="">

Introduction

Ever wondered what people are saying about certain countries? Whether it's in a positive/negative light? What are the most commonly used phrases/words to describe the country? In this dataset I present tweets where a certain country gets mentioned in the hashtags (e.g. #HongKong, #NewZealand). It contains around 150 countries in the world. I've added an additional field called polarity which has the sentiment computed from the text field. Feel free to explore! Feedback is much appreciated!

Content

Each row represents a tweet. Creation Dates of Tweets Range from 12/07/2020 to 25/07/2020. Will update on a Monthly cadence. - The Country can be derived from the file_name field. (this field is very Tableau friendly when it comes to plotting maps) - The Date at which the tweet was created can be got from created_at field. - The Search Query used to query the Twitter Search Engine can be got from search_query field. - The Tweet Full Text can be got from the text field. - The Sentiment can be got from polarity field. (I've used the Vader Model from NLTK to compute this.)

Notes

There maybe slight duplications in tweet id's before 22/07/2020. I have since fixed this bug.

Acknowledgements

Thanks to the tweepy package for making the data extraction via Twitter API so easy.

Shameless Plug

Feel free to checkout my blog if you want to learn how I built the datalake via AWS or for other data shenanigans.

Here's an App I built using a live version of this data.

The global number of Twitter users in was forecast to continuously increase between 2024 and 2028 by in total 74.3 million users (+17.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 503.42 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like South America and the Americas.

IFLA stands for The International Federation of Library Associations and Institutions. The IFLA World Library and Information Congress 2016 and 2nd IFLA General Conference and Assembly, ‘Connections. Collaboration. Community’ took place 13–19 August 2016 at the Greater Columbus Convention Center (GCCC) in Columbus, Ohio, United States. The official hashtag of the conference was #WLIC2016.This spreadsheet contains the results of a text analysis of 22327 Tweets publicly labeled with #WLIC2016 between Sunday 14 and Thursday 18 August 2015. The collection of the source dataset was made with a Twitter Archiving Google Spreadsheet and the automated text analysis was done with the Terms tool from Voyant Tools. The spreadsheet contains:A sheet containing a table summarising the source archive A sheet containing a table detailing tweet counts per day. Sheets containing the 'raw' (no stop words, no manual refining) tables of top 300 most frequent terms and their counts for the Sun-Thu corpus and each individual corpus (1 per day).Sheets containing the 'edited' (edited English stop word filter applied, manually refined) tables of top 50 Most frequent terms and their counts for the Sun-Thu corpus and each individual corpus (1 per day).A sheet containing a comparison table of the top 50 per day.Other ConsiderationsOnly Tweets published by accounts with at least one follower were included in the source archive.Both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (González-Bailon, Sandra, et al, 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that each and every Tweet tagged with #WLIC2016 during the indicated period was analysed. The dataset was shared for archival, comparative and indicative educational research purposes only.Only content from public accounts, obtained from the Twitter Search API, was analysed. The source data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.This file contains the results of analyses of Tweets that were published openly on the Web with the queried hashtag; the source Tweets are not included. The content of the source Tweets is responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually. This work is shared to archive, document and encourage open educational research into scholarly activity on Twitter. The resulting dataset does not contain complete Tweets nor Twitter metadata. No private personal information was shared. The collection, analysis and sharing of the data has been enabled and allowed by Twitter's Privacy Policy. The sharing of the results complies with Twitter's Developer Rules of the Road. A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (Tweets in this case). Tweets published publicly by scholars or other professionals during academic conferences are often publicly tagged (labeled) with a hashtag dedicated to the conference in question. This practice used to be the confined to a few 'niche' fields; it is increasingly becoming the norm rather than the exception. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting public professional networks that tend to observe scholarly practices and accepted modes of social and professional behaviour. In general terms it can be argued that scholarly Twitter users willingly and consciously tag their public Tweets with a conference hashtag as a means to network and to promote, report from, reflect on, comment on and generally contribute publicly to the scholarly conversation around conferences. As Twitter users, conference Twitter hashtag contributors have agreed to Twitter's Privacy and data sharing policies. Professional associations like the Modern Language Association and the American Pyschological Association recognise Tweets as citeable scholarly outputs. Archiving scholarly Tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection.Beyond individual Tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. Though this work has limitations and might not be thoroughly systematic, it is hoped it can contribute to developing new insights into a discipline's public concerns as expressed on Twitter over time.As it is increasingly recommended for data sharing, the CC-0 license has been applied to the resulting output in the repository. It is important however to bear in mind that some terms appearing in the dataset might be licensed individually differently; copyright of the source Tweets -and sometimes of individual terms- belongs to their authors. Authorial/curatorial/collection work has been performed on the shared file as a curated dataset resulting from analysis, in order to make it available as part of the scholarly record. If this dataset is consulted attribution is always welcome.Ideally for proper reproducibility and to encourage other studies the whole archive dataset should be available. Those wishing to obtain the whole Tweets should still be able to get them themselves via text and data mining methods.

This is a CSV file containing Tweet IDs of 3,805 Tweets from user ID 25073877 posted publicly between Thursday February 25 2016 16:35:12 +0000 to Monday April 03 2017 12:51:01 +0000.This file does not include Tweets' texts nor URLs. Columns in the file areid_strfrom_user_id_str created_at time source user_followers_count user_friends_count Motivations to Share this DataArchived Tweets can provide interesting insights for the study of contemporary history of media, politics, diplomacy, etc. The queried account is a public account widely agreed to be of exceptional national and international public interest. Though they provide public access to tweeted content in real time, Twitter Web and mobile clients are not suited for appropriate Tweet corpus analysis. For anyone researching social media, access to the data is absolutely essential in order to perform, review and reproduce studies. Archiving Tweets of public interest due to their historic significance is a means to both preserve and enable reproducible study of this form of rapid online communication that otherwise can very likely become unretrievable as time passes. Due to Twitter's current business model and API limits, to date collecting in real time is the only relatively reliable method to archive Tweets at a small scale. Methodology and LimitationsThe Tweets contained in this file were collected by Ernesto Priego using a Python script. The data collection search query was from:realdonaldtrump. A trigger was scheduled to collect atuomatically every hour. The original data harvesting was refined to delete duplications, to subscribe to Twitter's Terms and Conditions and so that the data was sorted in chronological order.Duplication of data due to the automated collection is possible so further data refining might be required. The file may not contain data from Tweets deleted by the queried user account immediately after original publication. Both research and experience show that the Twitter search API is not 100% reliable. (Gonzalez-Bailon, Sandra, et al. 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that this file contains each and every Tweet posted by the queried account during the indicated period. This file dataset is shared for archival, comparative and indicative educational research purposes only. The content included is from a public Twitter account and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.The original Tweets, their contents and associated metadata were published openly on the Web from the queried public account and are responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually.No private personal information is shared in this dataset. As indicated above this dataset does not contain the text of the Tweets. The collection and sharing of this dataset is enabled and allowed by Twitter's Privacy Policy. The sharing of this dataset complies with Twitter's Developer Rules of the Road.This dataset is shared to archive, document and encourage open educational research into political activity on Twitter.Other ConsiderationsAll Twitter users agree to Twitter's Privacy and data sharing policies. Social media research remains in its infancy and though work has been done to develop best practices there is yet no agreement on a series of grey areas relating to reseach methodologies including ad hoc social media specific research ethics guidelines for reproducible research. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time. Reproducibility is considered here a key value for robust and trustworthy research. Different scholarly professional associations like the Modern Language Association recognise Tweets, datasets and other online and digital resources as citeable scholarly outputs.The data contained in the deposited file is otherwise available elsewhere through different methods.

Replication package for the paper: "Unveiling Inclusiveness-Related User Feedback in Mobile Applications."

This dataset includes the user feedback (app reviews, Reddit, and Twitter posts) from 50 popular apps, used in our study on analyzing inclusiveness in user feedback. We also provide the prompts we used to conduct the classification of our user feedback.

Percentage of people claiming key benefits as a proportion of the working age population. The information is derived from count data which are already held on the Neighbourhood Statistics website: Working Age Client Group (August 2004) provided by the Department for Work and Pensions (DWP) and Mid-2004 small area population estimates data provided by the Office for National Statistics (ONS). The data includes breakdowns by statistical group, gender and 3 bands of age (16-24, 25-49 and 50 and over). The small area population estimates used to derived the percentages are also included in the dataset.

Source: Department for Work and Pensions (DWP)

Publisher: Neighbourhood Statistics

Geographies: Lower Layer Super Output Area (LSOA), Local Authority District (LAD), Government Office Region (GOR), National

Geographic coverage: England and Wales

Time coverage: 2001 to 2007

Type of data: Administrative data

comicsuf16 was the hashtag used for the 13th annual Conference on Comics and Graphic Novels, "Transnational Comics: Crossing Gutters, Transcending Boundaries,” which took place April 8th-10th 2016 at the University of Florida, Gainesville, Florida, USA.This is a .csv file containing approximately 249 unique tweets publicly

published with the hashtag #comicsuf16 during the indicated period (06/04/2016 15:55:58 - 10/04/2016 13:54:47 BST). The Tweets contained in this file were collected by Ernesto Priego using Martin Hawksey's TAGS 6.0. Only users with at least 10 followers were included in the archive. Retweets have been included. Data is likely to require refining and deduplication.Times under Column D are in UTC (conference local time); times under Column E are in BST (time of collection). Summertime changes may have not been reflected in the collection. Please note that both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012). It cannot be guaranteed this file contains each and every Tweet tagged with #comicsuf16 during the indicated period, and is shared for comparative and indicative educational research purposes only. The data is shared as is. The sharing of this dataset complies with Twitter's Developer Rules of the Road. Only content from public accounts is included and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.The profile_image_url and entities_str metadata were removed before public sharing.Each Tweet and its contents were published openly on the Web with the queried hashtag and are responsibility of the original authors. Tweets published publicly by scholars during academic conferences are often tagged (labeled) with a hashtag dedicated to the conference in question. The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (tweets in this case). A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting networks that tend to observe, more often than not, scholarly modes of behaviour. Generally it can be argued that scholarlyTwitter users tag their public tweets with a conference hashtag as a means to report from, comment on and generally contribute publicly to the scholarly conversation around conferences. Professional associations like the Modern Language Association recognise tweets as citeable scholarly outputs. Archiving scholarly tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection. Beyond individual tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. To date, collecting in real time is the only relatively accurate method to archive tweets at a small scale. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time. No sensitive information is contained in this dataset.This dataset is shared to archive, document and encourage open educational research into scholarly activity on Twitter.

These are the service requests created by 311, categorized by the channel on which they were submitted. 311 offers customers 4 channels to submit service requests: by calling 311, using the SF311 Mobile App, visiting our website, or messaging us through Twitter. This report differs from 311-Cases in that it includes all case types created in the SF311 system, but not cases created by other agencies. This dataset is updated on a monthly basis.

Context

Blockchain technology, first implemented by Satoshi Nakamoto in 2009 as a core component of Bitcoin, is a distributed, public ledger recording transactions. Its usage allows secure peer-to-peer communication by linking blocks containing hash pointers to a previous block, a timestamp, and transaction data. Bitcoin is a decentralized digital currency (cryptocurrency) which leverages the Blockchain to store transactions in a distributed manner in order to mitigate against flaws in the financial industry.

Nearly ten years after its inception, Bitcoin and other cryptocurrencies experienced an explosion in popular awareness. The value of Bitcoin, on the other hand, has experienced more volatility. Meanwhile, as use cases of Bitcoin and Blockchain grow, mature, and expand, hype and controversy have swirled.

Content

In this dataset, you will have access to information about blockchain blocks and transactions. All historical data are in the bigquery-public-data:crypto_bitcoin dataset. It’s updated it every 10 minutes. The data can be joined with historical prices in kernels. See available similar datasets here: https://www.kaggle.com/datasets?search=bitcoin.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_bitcoin.[TABLENAME]. Fork this kernel to get started.

Method & Acknowledgements

Allen Day (Twitter | Medium), Google Cloud Developer Advocate & Colin Bookman, Google Cloud Customer Engineer retrieve data from the Bitcoin network using a custom client available on GitHub that they built with the bitcoinj Java library. Historical data from the origin block to 2018-01-31 were loaded in bulk to two BigQuery tables, blocks_raw and transactions. These tables contain fresh data, as they are now appended when new blocks are broadcast to the Bitcoin network. For additional information visit the Google Cloud Big Data and Machine Learning Blog post "Bitcoin in BigQuery: Blockchain analytics on public data".

Photo by Andre Francois on Unsplash.

Inspiration

• How many bitcoins are sent each day?
• How many addresses receive bitcoin each day?
• Compare transaction volume to historical prices by joining with other available data sources

A Day in the Life of the Digital Humanities (Day of DH) is "an open community publication project that will bring together scholars interested in the digital humanities from around the world to document what they do on one day." Day of DH 2016 took place on April 8th and one of the hashtags used was #dayofDH2016 (variations including #dayofDH were also used). This is a .csv file containing approximately 2,252 unique tweets publicly published with the hashtag #dayofDH2016 during the indicated period.The Tweets contained in this file were collected by Ernesto Priego using Martin Hawksey's TAGS 6.0. Only users with at least 100 followers were included in the archive. Retweets have been included. Data is likely to require refining and deduplication.Please note that both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012). It cannot be guaranteed this file contains each and every Tweet tagged with #dayofDH2016 during the indicated period, and is shared for comparative and indicative educational research purposes only.The data is shared as is. The sharing of this dataset complies with Twitter's Developer Rules of the Road. Only content from public accounts is included and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.The profile_image_url and entities_str metadata were removed before public sharing.Each Tweet and its contents were published openly on the Web with the queried hashtag and are responsibility of the original authors.Tweets published publicly by scholars during academic conferences are often tagged (labeled) with a hashtag dedicated to the conference in question. The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (tweets in this case). A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting networks that tend to observe, more often than not, scholarly modes of behaviour. Generally it can be argued that scholarlyTwitter users tag their public tweets with a conference hashtag as a means to report from, comment on and generally contribute publicly to the scholarly conversation around conferences. Professional associations like the Modern Language Association recognise tweets as citeable scholarly outputs. Archiving scholarly tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection. Beyond individual tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. To date, collecting in real time is the only relatively accurate method to archive tweets at a small scale. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time.No sensitive information is contained in this dataset.This dataset is shared to archive, document and encourage open educational research into scholarly activity on Twitter.

The Academic Publishing in Europe 12 (APE 2017) conference took place in Berlin, Germany, on 17 - 18 January 2017 with a Pre-Conference Day on 16 January 2017. A hashtag used on Twitter to report and discuss from / about the conference was #APE2017.What This Output IsThis is a CSV file containing a total of 2,011 Tweets publicly published with the hashtag #APE2017 between Monday January 16 2017 at 00:48:59 +0000 and Wednesday January 18 2017 22:42:06 +0000. Please note the conference took place in Berlin (GMT +1); the local time of publishing appears under column E. Methodology and LimitationsThe Tweets contained in this file were collected by Ernesto Priego using Martin Hawksey's TAGS 6.0. The original data collection gathered 2,133 Tweets including a period covering 10-19 January 2017. For the purpose of this particular dataset the original dataset was refined to include here only the conference period of 16-18 January and the data was re-ordered in chronological order. Retweets have been included (Retweets count as Tweets), so Tweet text duplication is normal. The collection spreadsheet was customised to reflect the time zone and geographical location of the conference and GMT (columns D and E).The profile_image_url and entities_str metadata were removed before public sharing in this archive. Though initial data refining was conducted please bear in mind that the conference hashtag might have been spammed so some Tweets colllected may be from spam accounts. Some automated refining has been performed to remove Tweets not related to the conference but the data is likely to require further refining and deduplication. Both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (Gonzalez-Bailon, Sandra, et al. 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that this file contains each and every Tweet tagged with #APE2017 during the indicated period, and the dataset is shared for archival, comparative and indicative educational research purposes only. Other hashtag combinations might have been used for the conference as well. Only content from public accounts is included and was obtained from the Twitter Search API. The shared data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.Each Tweet and its contents were published openly on the Web with the queried hashtag and are responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually. No private personal information is shared in this dataset. The collection and sharing of this dataset is enabled and allowed by Twitter's Privacy Policy. The sharing of this dataset complies with Twitter's Developer Rules of the Road. This dataset is shared to archive, document and encourage open educational research into scholarly activity on Twitter. Other ConsiderationsTweets published publicly by scholars during academic conferences are often tagged (labeled) with a hashtag dedicated to the conference in question.The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (Tweets in this case). A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting public professional networks that tend to observe scholarly practices and accepted modes of social and professional behaviour. In general terms it can be argued that scholarly Twitter users willingly and consciously tag their public Tweets with a conference hashtag as a means to network and to promote, report from, reflect on, comment on and generally contribute publicly to the scholarly conversation around conferences. As Twitter users, conference Twitter hashtag contributors have agreed to Twitter's Privacy and data sharing policies. Professional associations like the Modern Language Association recognise Tweets as citeable scholarly outputs. Archiving scholarly Tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection.Beyond individual tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. To date, collecting in real time is the only relatively accurate method to archive tweets at a small scale. Though these datasets have limitations and are not thoroughly systematic, it is hoped they can contribute to developing new insights into the discipline's presence on Twitter over time.The CC-BY license has been applied to the output in the repository as a curated dataset. Authorial/curatorial/collection work has been performed on the file in order to make it available as part of the scholarly record. The data contained in the deposited file is otherwise freely available elsewhere through different methods and anyone not wishing to attribute the data to the creator of this output is needless to say free to do their own collection and clean their own data.

The number of Twitter users in Kenya was forecast to continuously increase between 2024 and 2028 by in total 3.6 million users (+112.15 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 6.78 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Zambia and Rwanda.

The information in this dataset refers to numbers of Working Age Benefit Claimants and is derived from a 100% data source; the Work and Pensions Longitudinal Study (WPLS). The dataset provides counts of benefit claimants categorised by their statistical group (their main reason for interacting with the benefit system), gender and age. https://www.nomisweb.co.uk/query/construct/summary.asp?mode=construct&version=0&dataset=105

Source: Department for Work and Pensions (DWP)

Publisher: Department for Work and Pensions (DWP)

Geographies: Lower Layer Super Output Area (LSOA), Middle Layer Super Output Area (MSOA), Ward, Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: Great Britain

Time coverage: 1999 to 2009

Type of data: Administrative data

Notes: The main advantage of this dataset is that the double counting of claimants of multiple benefits has been removed so that users will get a more accurate picture of benefit claiming and worklessness at a small area level.

Analysis of ‘311 Cases by Channel’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/32571d36-798a-4ffd-8972-b19b46bd38c3 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

These are the service requests created by 311, categorized by the channel on which they were submitted. 311 offers customers 4 channels to submit service requests: by calling 311, using the SF311 Mobile App, visiting our website, or messaging us through Twitter. This report differs from 311-Cases in that it includes all case types created in the SF311 system, but not cases created by other agencies. This dataset is updated on a monthly basis.

--- Original source retains full ownership of the source dataset ---

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

This feature class contains points indicating the centroid of the 189 TMKs in the state of Hawaii in which Hawaii DOH has approved a wastewater treatment plant, officially categorized as "Use Approved" as of August 2017. These features are incorporated in the U.S. EPA Region 9 Hawaii Wastewater Mapping application, a user interface mapping tool to help manage the Large Capacity Cesspool Program compliance and outreach efforts and assist with inspection targeting in Hawaii. The application can be found on the EPA GeoPlatform at: "https://epa.maps.arcgis.com/apps/webappviewer/index.html?id=afd05fc3ab2347b2bcc63c5c20f59926" https://epa.maps.arcgis.com/apps/webappviewer/index.html?id=afd05fc3ab2347b2bcc63c5c20f59926