In the first quarter of 2020, Twitter's clickthrough rate (CTR) stood at **** percent, up from **** percent in the corresponding quarter of 2019. In 2020, Twitter's ad revenue amounted to **** billion U.S. dollars.

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.

SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality.
SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.
This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.
The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility.
As part of writing a chapter for the SSH CENTRE book on ‘Mobility’, we set out to analyse the sentiment of users on Twitter regarding shared and active mobility modes in Brussels. This involved us collecting tweets between 2017-2022. A tweet was collected if it contained a previously defined mobility keyword (for example: metro) and either the name of a (local) politician, a neighbourhood or municipality, or a (shared) mobility provider. The files attached to this Zenodo webpage is a csv files containing the tweets collected.”.

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

ReLDI-NormTagNER-hr 3.0 is a manually annotated corpus of Croatian tweets. It is meant as a gold-standard training and testing dataset for tokenisation, sentence segmentation, word normalisation, morphosyntactic tagging, lemmatisation and named entity recognition of non-standard Croatian. Each tweet is also annotated for its automatically assigned standardness levels (T = technical standardness, L = linguistic standardness).

This version of the dataset has various annotation errors corrected and the dataset encoded in the CoNLL-U-Plus format, similar to other manually annotated linguistic datasets for Croatian and Serbian.

The continuous improvement of this dataset is led by the CLASSLA knowledge centre for South Slavic languages (https://www.clarin.si/info/k-centre/) and the ReLDI Centre Belgrade (https://reldi.spur.uzh.ch).

This is a multimodal dataset used in the paper "On the Role of Images for Analyzing Claims in Social Media", accepted at CLEOPATRA-2021 (2nd International Workshop on Cross-lingual Event-centric Open Analytics), co-located with The Web Conference 2021.

The four datasets are curated for two different tasks that broadly come under fake news detection. Originally, the datasets were released as part of challenges or papers for text-based NLP tasks and are further extended here with corresponding images.

1. clef_en and clef_ar are English and Arabic Twitter datasets for claim check-worthiness detection released in CLEF CheckThat! 2020 Barrón-Cedeno et al. [1].
2. lesa is an English Twitter dataset for claim detection released by Gupta et al.[2]
3. mediaeval is an English Twitter dataset for conspiracy detection released in MediaEval 2020 Workshop by Pogorelov et al.[3]

The dataset details like data curation and annotation process can be found in the cited papers.

Datasets released here with corresponding images are relatively smaller than the original text-based tweets. The data statistics are as follows: 1. clef_en: 281 2. clef_ar: 2571 3. lesa: 1395 4. mediaeval: 1724

Each folder has two sub-folders and a json file data.json that consists of crawled tweets. Two sub-folders are: 1. images: This Contains crawled images with the same name as tweet-id in data.json. 2. splits: This contains 5-fold splits used for training and evaluation in our paper. Each file in this folder is a csv with two columns .

Code for the paper: https://github.com/cleopatra-itn/image_text_claim_detection

If you find the dataset and the paper useful, please cite our paper and the corresponding dataset papers[1,2,3] Cheema, Gullal S., et al. "On the Role of Images for Analyzing Claims in Social Media" 2nd International Workshop on Cross-lingual Event-centric Open Analytics (CLEOPATRA) co-located with The Web Conf 2021.

[1] Barrón-Cedeno, Alberto, et al. "Overview of CheckThat! 2020: Automatic identification and verification of claims in social media." International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, Cham, 2020. [2] Gupta, Shreya, et al. "LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content." arXiv preprint arXiv:2101.11891 (2021). [3] Pogorelov, Konstantin, et al. "FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020." MediaEval 2020 Workshop. 2020.

ReLDI-NormTagNER-sr 3.0 is a manually annotated corpus of Serbian tweets. It is meant as a gold-standard training and testing dataset for tokenisation, sentence segmentation, word normalisation, morphosyntactic tagging, lemmatisation and named entity recognition of non-standard Serbian. Each tweet is also annotated for its automatically assigned standardness levels (T = technical standardness, L = linguistic standardness).

This version of the dataset has various annotation mistakes corrected, and is now encoded in the CoNLL-U-Plus format, as are other linguistic training datasets for Croatian and Serbian.

The continuous improvement of this dataset is led by the CLASSLA knowledge centre for South Slavic languages (https://www.clarin.si/info/k-centre/) and the ReLDI Centre Belgrade (https://reldi.spur.uzh.ch).

This paper describes a large global dataset on people’s discourse and responses to the COVID-19 pandemic over the Twitter platform. From 28 January 2020 to 1 June 2022, we collected and processed over 252 million Twitter posts from more than 29 million unique users using four keywords: “corona”, “wuhan”, “nCov” and “covid”. Leveraging probabilistic topic modelling and pre-trained machine learning-based emotion recognition algorithms, we labelled each tweet with seventeen attributes, including a) ten binary attributes indicating the tweet’s relevance (1) or irrelevance (0) to the top ten detected topics, b) five quantitative emotion attributes indicating the degree of intensity of the valence or sentiment (from 0: extremely negative to 1: extremely positive) and the degree of intensity of fear, anger, sadness and happiness emotions (from 0: not at all to 1: extremely intense), and c) two categorical attributes indicating the sentiment (very negative, negative, neutral or mixed, positive, very positive) and the dominant emotion (fear, anger, sadness, happiness, no specific emotion) the tweet is mainly expressing. We discuss the technical validity and report the descriptive statistics of these attributes, their temporal distribution, and geographic representation. The paper concludes with a discussion of the dataset’s usage in communication, psychology, public health, economics, and epidemiology.

"In metrics we trust? Prospects & pitfalls of new research metrics" was a one-day workshop hosted by the University of Sussex, as part of the Independent Review of the Role of Metrics in Research Assessment. It took place on Tuesday 7 October 2014 at the Terrace Room, Conference Centre, Bramber House, University of Sussex, UK. This file contains a dataset of 1178 Tweets tagged with #HEFCEmetrics (case not sensitive). These Tweets were published publicly and tagged with #HEFCEmetrics between 02/10/2014 10:18 and 08/10/2014 00:27 GMT. This file was created and shared by Ernesto Priego (Centre for Information Science, City University London) with a Creative Commons- Attribution license (CC-BY) for academic research and educational use. The Tweets contained in this file were collected using Martin Hawksey’s TAGS 6.0. This file contains 3 sheets. Only users with at least 2 followers were included in the archive. Retweets have been included. An initial automatic deduplication was performed but data might require further deduplication. The Time column (D) has times in British Summer Time (BST). Please note that both research and experience show that the Twitter search API isn't 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-Bailón, Sandra, et al. 2012). It is not guaranteed this file contains each and every Tweet tagged with #HEFCEmetrics during the indicated period, and is shared for comparative and indicative educational and research purposes only. Please note the data in this file is likely to require further refining and even deduplication. The data is shared as is. The contents of each Tweet are responsibility of the original authors. This dataset is shared to encourage open research into scholarly activity on Twitter. If you use or refer to this data in any way please cite and link back using the citation information above. Contact:

Tweets about COVID-19 from sources in British Columbia. This dataset includes tweets from government officials, health authorities and journalists. The tweet IDs were collected using Documenting the Now's Twarc library (https://github.com/DocNow/twarc). The date of the earliest available tweet is different for each handle. The date of the latest available tweet will not be later than the upload date for each file. See the file-level information below. The tweet ids were extracted from the raw JSON files retrieved from Twitter using Twarc. However, Twitter's terms of use do not permit the sharing of the raw JSON files for this dataset. The raw JSON files can be retrieved from Twitter, provided the content is still available, using the 'hydrate command within Twarc. The researchers retained the source JSON files and may be contacted by other researchers if they wish to access them. The files of tweet ids will be updated over time and this metadata, the files and this readme.txt file will be updated accordingly. Raw JSON files were harvested using Twarc's 'timeline' command. The 'timeline' command retrieves the most recent tweets from the specified handle, to a maximum of approximately 3,300 tweets. The data for each handle was collected approximately weekly, starting in January 2021. In order not to lose earlier tweets, we concatenated the JSON for each new 'timeline' crawl to the earlier crawls and de-duplicated the combined JSON using Twarc's 'deduplicate' command. We then used Twarc's 'dehydrate' command to extract just the tweet ids from the deduplicate JSON file. Finally, we sorted the tweet ids numerically so that they would appear in ascending date order. The basic workflow looks like: twarc timeline --> concatenate JSON files --> deduplicate resulting JSON file --> dehydrate tweet ids --> sort tweet ids. The Twitter handles include: @BCGovNews: BC Government News. Tweets in this file start on 2019-06-06. @CDCofBC: BC Centre for Disease Control. Tweets in this file start on 2019-06-28. @Fraserhealth: Fraser Health Authority. Tweets in this file start on 2019-01-07. @ImmunizeBC: Evidence-based immunization information and tools for BC residents from the BC Centre for Disease Control. Tweets in this file start on 2014-11-19. @Interior_Health: Health authority for the Southern Interior of BC. Tweets in this file start on 2017-06-30. @Northern_Health: Health authority for the Northern Interior of BC. Tweets in this file start on 2018-07-01. @PHSAofBC: Provincial Health Services Authority of BC. Tweets in this file start on 2019-11-01. @SAHoffman: Suzanne Hoffman, Superintendent of Schools for Vancouver. Tweets in this file start on 2009-08-14. @VCHhealthcare: Vancouver Coastal Health Authority. Tweets in this file start on 2019-02-15. @VanIslandHealth: Vancouver Island Health Authority. Tweets in this file start on 2017-12-13. @adriandix: Adrian Dix, Member of the Legislative Assembly for Vancouver-Kingsway and BC Minister of Health. Tweets in this file start on 2019-09-13. @fnha: First Nations Health Authority. Tweets in this file start on 2017-05-03. @govTogetherBC: Government of BC citizen engagement. Tweets in this file start on 2016-09-03. @j_mcelroy: Municipal Affairs Reporter for CBC Vancouver. Tweets in this file start on 2021-01-04. @jordantinney: Jordan Tinney, Superintendent of Schools for Surrey. Tweets in this file start on 2013-01-16. @keithbaldrey: Keith Baldrey, Political journalist for Global TV, British Columbia. Tweets in this file start on 2020-12-14. @kennedystewart: Kennedy Stewart, 40th Mayor of Vancouver. Tweets in this file start on 2016-10-16. @richardzussman: Reporter for Global TV, British Columbia, at the provincial legislature. Tweets in this file start on 2020-12-31.

A freeform chart that shows % of U.S. adults who say they regularly get news from each social media site

The project explores conversations occurring on Twitter concerned with the passage through Parliament of the Trade Union Bill in 2015/16. The bill was the subject of a good deal of scrutiny from trade unions, their members and a variety of other individuals and groups. Collecting and analysing tweets containing the hashtag '#TUBill' over a period of two months formulated new insights into the patterns of connectivity between key actors in the Twitter network as well as the content of these online conversations.

This proposal is for a National Research Centre (WISERD/Civil Society) to undertake a five year programme of policy relevant research addressing Civil Society in Wales. Established in 2008, WISERD provides an 'All-Wales' focus for research and has had a major impact on the quantity and quality of social science research undertaken in Wales. As part of WISERD, WISERD/Civil Society will enable this work to be deepened and sustained through a focused research programme that further develops our research expertise, intensifies our policy impact and knowledge exchange work and strengthens our research capacity and career development activities. WISERD/Civil Society will therefore aim to develop key aspects of the multidisciplinary research initiated during the first phase of WISERD's work to produce new empirical evidence to inform our understanding of the changing nature of civil society in the context of devolved government and processes of profound social and economic change. There are many disagreements over what civil society is and how it may be changing. We do know that over the last forty years there have been unprecedented changes in the spheres of economy and industry, politics and governance, social relations and individual life courses. How individuals in local contexts are affected by and respond to dramatic institutional changes is not well understood. An important gap in our knowledge is in describing and explaining the impact of social change on local forms of civil society and civil society organisations and what this means for social cohesion and well-being. In addition how different forms of civil society are developing in the context of multi-level and devolved government is not well understood. Because of its size and devolved government, Wales offers a unique context for studying these issues. Viewing Wales as a 'laboratory for social science' the proposed centre will build on existing networks of researchers who have a wide range of expertise and skills. Large survey data sets will be exploited and analysed and new data collected on civil society in Wales, the UK and Europe. Inter-disciplinarity and multi-method approaches applied to longitudinal and comparative data will be a key feature and strength of the WISERD/Civil Society research programme. Our research will be underpinned by three principles: (i) to maximise research impact, (ii) to become a centre of excellence for comparative, longitudinal, and relational research methods and (iii) to contribute to the growth of research capacity in Wales. We will also extend our research out from Wales to undertake comparative studies at different regional, national and international levels. In this way WISERD will make substantive and novel contributions to the advancement of social theory applied to researching contemporary civil society and to methodological approaches to describing and explaining patterns of civic participation in the context of devolution and multi-level governance. Substantive research will be applied to real and timely research problems conducted under four inter-related themes: 1) Locality, Community and Civil Society 2) Individuals, Institutions and Governance 3) Economic Austerity, Social Enterprise and Inequality 4) Generation, Life Course and Social Participation. Our aim will be to produce a wide range of outputs accessible to a variety of different audiences, including: academic papers; books; working papers; seminars; web based material; video and e-learning materials; as well as disseminating our work through a diversity of activities. Public awareness will be raised through events; activities; and exhibitions, designed to foster interest and encourage discussion and debate. WISERD/Civil Society will have a strong management structure, substantial institutional support, and close links with relevant organisations, and will provide substantive career development for new and early-career researchers and PhD students.

Data Dictionary

Dataset Overview

• Rows: 10,000 campaigns
• Columns: 41 variables (35 base + 6 derived metrics)
• Date Range: January 1, 2024 - January 31, 2026
• Purpose: Multiple linear regression analysis of advertising campaign profitability

Variables

Campaign Identification (2)

Variable	Type	Description	Values
campaign_id	String	Unique campaign identifier	CAMP_00001 - CAMP_10000
campaign_objective	Categorical	Primary campaign goal	Brand Awareness, Lead Generation, Conversions, App Installs, Engagement

Platform & Placement (4)

Variable	Type	Description	Values
platform	Categorical	Advertising platform	Google Ads, Facebook, LinkedIn, TikTok, Twitter, Instagram
ad_placement	Categorical	Ad display location	Feed, Stories, Search, Display Network, In-Stream Video, Sidebar
device_type	Categorical	User device	Desktop, Mobile, Tablet
operating_system	Categorical	Device OS	iOS, Android, Windows, macOS, Other

Creative Attributes (6)

Variable	Type	Description	Values
creative_format	Categorical	Ad format type	Video, Image, Carousel, Text, Interactive, Story
creative_size	Categorical	Ad dimensions (pixels)	1080x1080, 1920x1080, 300x250, 728x90, 320x50, 1200x628
ad_copy_length	Categorical	Text length category	Short, Medium, Long
has_call_to_action	Boolean	CTA button present	True, False
creative_emotion	Categorical	Emotional tone	Fear, Joy, Urgency, Trust, Curiosity, Neutral
creative_age_days	Integer	Days since creative launch	1-90

Audience Targeting (6)

Variable	Type	Description	Values
target_audience_age	Categorical	Age bracket	18-24, 25-34, 35-44, 45-54, 55-64, 65+
target_audience_gender	Categorical	Gender targeting	Male, Female, All
audience_interest_category	Categorical	Interest segment	Tech Enthusiasts, Business Professionals, Gamers, Students, Shoppers, Health & Fitness
income_bracket	Categorical	Household income	<$50K, $50K-$100K, $100K-$200K, >$200K
purchase_intent_score	Categorical	Behavioral intent signal	Low, Medium, High
retargeting_flag	Boolean	Retargeting campaign	True, False

Temporal (5)

Variable	Type	Description	Range
start_date	Date	Campaign start date	2024-01-01 to 2026-01-31
quarter	Integer	Calendar quarter	1-4
day_of_week	Categorical	Day campaign ran	Monday-Sunday
hour_of_day	Integer	Hour ad shown (24-hour)	0-23
campaign_day	Integer	Day in campaign lifecycle	1-90

Auction & Quality (2)

Variable	Type	Description	Range
quality_score	Integer	Platform quality rating	1-10
actual_cpc	Float	Actual cost per click paid ($)	$0.25 - $17.00

Performance Metrics (5)

Variable	Type	Description	Range	Constraints
impressions	Integer	Times ad displayed	5,000 - 500,000	-
clicks	Integer	Number of ad clicks	10+	≤ impressions
conversions	Integer	Completed actions	0+	≤ clicks
ad_spend	Float	Campaign spend ($)	Variable	clicks × actual_cpc
revenue	Float	Revenue generated ($)	$0+	-

Engagement Quality (3)

Variable	Type	Description	Range
bounce_rate	Float	% immediate exits	10.0 - 90.0
avg_session_duration_seconds	Integer	Average time on site (seconds)	10 - 600
pages_per_session	Float	Average pages viewed	1.0 - 10.0

Industry Context (2)

Variable	Type	Description	Values
industry_vertical	Categorical	Business sector	SaaS, E-commerce, Healthcare, Finance, Education, Gaming
budget_tier	Categorical	Budget classification	Low, Medium, High

Derived Metrics (6)

Variable	Formula	Description
CTR	(clicks / impressions) × 100	Click-through rate (%)
CPC	ad_spend / clicks	Cost per click ($)
conversion_rate	(conversions / clicks) × 100	Conversion rate (%)
CPA	ad_spend / conversions	Cost per acquisition ($)
ROAS	revenue / ad_spend	Return on ad spend (multiplier)
profit	revenue - ad_spend	PRIMARY DEPENDENT VARIABLE ($)

Performance Benchmarks (2025 Industry Data)

| Metric | This Dataset | Industry Benchmark | Source | |--------|-...

SSIX BREXIT Gold Standard

This repository contains the BREXIT Twitter Gold Standard produced by the SSIX Project https://ssix-project.eu/.

Only a sample is available here, to rebuild the full dataset, follow the instructions on the SSIX Project code repository:

https://bitbucket.org/ssix-project/brexit-gold-standard

This dataset was collected as part of the Kili-SES research unit, Subproject 2, Work Package 2, which focuses on understanding the elements of nature that support non-material contributions, such as aesthetic enjoyment, recreation, and spirituality. Using the NCP paradigm, non-material contributions of nature to people (NCP) were identified through an inductive approach, together with the entities of nature (EN) mentioned in Twitter posts.

This dataset builds on https://doi. org/10.5281/zenodo.14217191 by including those specific EN (and human-made features) mentioned by tourists on their Twitter posts - in order to enable evaluation of NCP-EN associations. Overview: this dataset was collected in October 2021 using the R package academictwitteR with Twitter’s official Application Programming Interface keys (API; https://developer.twitter.com/en/docs). These are publicly available Tweets targeting Mount Kilimanjaro National Park (Tanzania) and its surroundings from 10 years (April 2011 to June 2021). Data were manually filtered and cleaned to preserve tourists' Tweets of their nature experiences at Mount Kilimanjaro, Tanzania. These final dataset contains 1255 entries, and each observation represents a Tweet that contains at least one non-material nature's contributions to people (non-material NCP) and one entity of nature mentioned by an individual.Please refer to the README document to understand the data structure of this dataset, and the corresponding codes to characterize each non-material NCP as either specialized or generalized. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Research Unit 5064: “The role of nature for human well-being in the Kilimanjaro Social-Ecological System (Kili-SES)” (Project Number 428658210).

The 'McStrike' project was developed as a response to a forthcoming strike by McDonald's employees in the UK on the 4th September 2017, organised by the Bakers, Food and Allied Workers Union (BFAWU). All tweets containing the hashtag '#McStrike' over a period of 21 days before, during and after the strike were collected. Applying qualitative content analysis and social network analysis techniques, the project contributed new insights into the processes and practices of online collective action.

This proposal is for a National Research Centre (WISERD/Civil Society) to undertake a five year programme of policy relevant research addressing Civil Society in Wales. Established in 2008, WISERD provides an 'All-Wales' focus for research and has had a major impact on the quantity and quality of social science research undertaken in Wales. As part of WISERD, WISERD/Civil Society will enable this work to be deepened and sustained through a focused research programme that further develops our research expertise, intensifies our policy impact and knowledge exchange work and strengthens our research capacity and career development activities. WISERD/Civil Society will therefore aim to develop key aspects of the multidisciplinary research initiated during the first phase of WISERD's work to produce new empirical evidence to inform our understanding of the changing nature of civil society in the context of devolved government and processes of profound social and economic change. There are many disagreements over what civil society is and how it may be changing. We do know that over the last forty years there have been unprecedented changes in the spheres of economy and industry, politics and governance, social relations and individual life courses. How individuals in local contexts are affected by and respond to dramatic institutional changes is not well understood. An important gap in our knowledge is in describing and explaining the impact of social change on local forms of civil society and civil society organisations and what this means for social cohesion and well-being. In addition how different forms of civil society are developing in the context of multi-level and devolved government is not well understood. Because of its size and devolved government, Wales offers a unique context for studying these issues. Viewing Wales as a 'laboratory for social science' the proposed centre will build on existing networks of researchers who have a wide range of expertise and skills. Large survey data sets will be exploited and analysed and new data collected on civil society in Wales, the UK and Europe. Inter-disciplinarity and multi-method approaches applied to longitudinal and comparative data will be a key feature and strength of the WISERD/Civil Society research programme. Our research will be underpinned by three principles: (i) to maximise research impact, (ii) to become a centre of excellence for comparative, longitudinal, and relational research methods and (iii) to contribute to the growth of research capacity in Wales. We will also extend our research out from Wales to undertake comparative studies at different regional, national and international levels. In this way WISERD will make substantive and novel contributions to the advancement of social theory applied to researching contemporary civil society and to methodological approaches to describing and explaining patterns of civic participation in the context of devolution and multi-level governance. Substantive research will be applied to real and timely research problems conducted under four inter-related themes: 1) Locality, Community and Civil Society 2) Individuals, Institutions and Governance 3) Economic Austerity, Social Enterprise and Inequality 4) Generation, Life Course and Social Participation. Our aim will be to produce a wide range of outputs accessible to a variety of different audiences, including: academic papers; books; working papers; seminars; web based material; video and e-learning materials; as well as disseminating our work through a diversity of activities. Public awareness will be raised through events; activities; and exhibitions, designed to foster interest and encourage discussion and debate. WISERD/Civil Society will have a strong management structure, substantial institutional support, and close links with relevant organisations, and will provide substantive career development for new and early-career researchers and PhD students.

This .XLS file contains a dataset of Tweets tagged with #dh2013 (case not sensitive). This file was created and shared by Ernesto Priego (Centre for Information Science, City University London) with a Creative Commons- Attribution license (CC-BY) for academic research and educational use. The Digital Humanities 2013 conference took place at the University of Nebraska–Lincoln, USA, 16-19 July 2013. The file contains approximately 6,661 Tweets published publicly and tagged with #dh2013 between Mon Jul 15 07:12:10 +0000 and Sat Jul 20 23:20:04 +0000. Plase note the data on this set is incomplete. If you have the missing Tweets, will you let us know to complete a set? The Tweets contained in this file were originally collected in July 2013 using Martin Hawksey’s TAGS 5.1. Due to the volume of Tweets several Google Spreadsheets were created during preceding and during the event, which were subsequently refined to individual sheets. An attempt to reconstruct the chronology was done manually. With thanks to Lisa Rhody who contributed some Tweets I had failed to collect. Sheet 0. A 'Cite Me' sheet, including procedence of this file, citation information, information about its contents, the methods employed and some context. Sheet 1. Monday 15 July 2013 ( 371 Tweets; noticeably incomplete) Sheet 2. Tuesday 16 July 2013 ( 1, 187 Tweets) Sheet 3. Wednesday 17 July 2013 ( 2, 227 Tweets) Sheet 4. Thursday 18 July 2013 ( 2, 826 Tweets) Sheet 5. Friday 19 July 2013 (approx 1500 Tweets; various Tweets with line breaks; noticeably incomplete due to high volumes and collection times; set from Fri Jul 19 13:41:01 +0000 ) Sheet 6. Saturday 20 July 2013 ( 122 Tweets; incomplete, set starts from Sat Jul 20 17:42:30) Times are, unfortunately, in GMT (created) and BST (time). They should be Nebraska time, though of course not all Tweets were tweeted from the conference location. This means that dates do not correspond with Conference day times due to time difference. Nebraska is CDT. Only users with at least 2 followers were included in the archive. Retweets have been included. Data might require reduplication. Due to the different methods employed in attempting to catch a high volume of Tweets, unfortunately the metadata in the set is not complete (the lack of ISO language metadata in most of these sheets is particularly disappointing, as it would have provided interesting insights).. Some work was done to ensure the chronology was complete; I have highlighted gaps in the Tweets on yellow on the sheets and in the listing above. Please note that both research and experience show that the Twitter search API isn't 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-Bailón, Sandra, et al. 2012). The Tweet volume was higher than what the available collecting methods allowed so data in this file is known to be incomplete. It is not guaranteed this file contains each and every Tweet tagged with #dh2013 during the indicated period, and is shared for comparative and indicative educational and research purposes only. Please note the data in this file is likely to require further refining and even deduplication. The data is shared as is. This dataset is shared to encourage open research into scholarly activity on Twitter. If you use or refer to this data in any way please cite and link back using the citation information above.

National Rail Enquiries (of the Rail Delivery Group) provides customer information for National Rail passenger services in England, Scotland and Wales. This table is a record of the Twitter handles used by National Rail Enquiries in communicating unplanned disruptions across the network, which is used by Transport for West Midlands to geolocate disruptions impacted stations to our customers. This table includes the name of the station in Great Britain, appended with the Twitter handle sourced from previous disruptions. To be used only by the Transport for West Midlands Regional Transport Coordination Centre team only.

The HEFCE metrics workshop: metrics and the assessment of research quality and impact in the arts and humanities took place on Friday 16 January 2015, 1030 to 1630 GMT at the Scarman Conference Centre, University of Warwick, UK. This file contains a dataset of 821 Tweets tagged with #HEFCEmetrics (case not sensitive). These Tweets were publicly published and tagged with #HEFCEmetrics between 16/01/2015 00:35:08 GMT and 16/01/2015 23:19:33 GMT. The collection period corresponds to the day the workshop took place in real time. This file was created and shared by Ernesto Priego (Centre for Information Science, City University London) with a Creative Commons- Attribution license (CC-BY) for academic research and educational use. The Tweets contained in this file were collected using Martin Hawksey's TAGS 6.0. This file contains 2 sheets. Only users with at least 2 followers were included in the archive. Retweets have been included. An initial automatic deduplication was performed but data might require further 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 is not guaranteed this file contains each and every Tweet tagged with #HEFCEmetrics during the indicated period, and is shared for comparative and indicative educational and research purposes only. Please note the data in this file is likely to require further refining and even deduplication. The data is shared as is. The contents of each Tweet are responsibility of the original authors. This dataset is shared to encourage open research into scholarly activity on Twitter. If you use or refer to this data in any way please cite and link back using the citation information above. For the #HEFCEmetrics Twitter archive corresponding to the one-day workshop hosted by the University of Sussex on Tuesday 7 October 2014, please go to Priego, Ernesto (2014): A #HEFCEmetrics Twitter Archive. figshare.http://dx.doi.org/10.6084/m9.figshare.1196029

Country - India; Parties Considered - the Indian National Congress (INC) and the Bharatiya Janata Party (BJP), the two major national political parties; Time period considered - Feb 1, 2019 to Feb 10, 2019. Research questions. The research was intended to find answer for the following questions: 1. Which political party uses Twitter extensively? 2. What were the major issues discussed by political parties in Twitter? 3. Were the issues tweeted by political parties and politicians reflect the ground situation? 4. What was the most prevalent sentiment in the Tweets made by political parties?

Findings: Results show that the BJP has a stronger twitter base and uses twitter extensively to connect with its followers when compared to the INC. The topics discussed by the both the parties encompassed a wide range of issues and schemes ranging from agriculture to national security. The issues and schemes raised by the INC is seen to have more resonance among people than the issues and schemes raised by the BJP i.e., the schemes and issues raised by the INC appeared to be more relevant and impactful on people’s lives than the ones raised by the BJP. Despite the BJP enjoying a stronger follower base in Twitter alongside with its extensive usage of Twitter to connect and interact with its followers when compared to the INC it seems to be trailing behind the INC in the race to 2019 assembly elections as there is almost a 30% gap between it and the INC when it comes to the resonance enjoyed by the issues and schemes proposed by them which at the end of the day would prove instrumental in determining people’s electoral preferences. So, based on the analysis of the data collected in this study the INC seems to have a clear edge over the BJP in forming the next government at the centre. Neutral sentiment was found to be the most prevalent sentiment in the tweets posted by the both the parties overall but since neutral sentiments are assigned a sentiment score of 0, they don’t affect the cumulative sentiment of all the tweets analysed which was found to be negative in both the cases of the BJP as well as the INC.