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.

The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.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).

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, 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 and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.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 Reddit users in countries like Mexico and Canada.

Tiktok network graph with 5,638 nodes and 318,986 unique links, representing up to 790,599 weighted links between labels, using Gephi network analysis software.

Source of:

Peña-Fernández, Simón, Larrondo-Ureta, Ainara, & Morales-i-Gras, Jordi. (2022). Current affairs on TikTok. Virality and entertainment for digital natives. Profesional De La Información, 31(1), 1–12. https://doi.org/10.5281/zenodo.5962655

Abstract:

Since its appearance in 2018, TikTok has become one of the most popular social media platforms among digital natives because of its algorithm-based engagement strategies, a policy of public accounts, and a simple, colorful, and intuitive content interface. As happened in the past with other platforms such as Facebook, Twitter, and Instagram, various media are currently seeking ways to adapt to TikTok and its particular characteristics to attract a younger audience less accustomed to the consumption of journalistic material. Against this background, the aim of this study is to identify the presence of the media and journalists on TikTok, measure the virality and engagement of the content they generate, describe the communities created around them, and identify the presence of journalistic use of these accounts. For this, 23,174 videos from 143 accounts belonging to media from 25 countries were analyzed. The results indicate that, in general, the presence and impact of the media in this social network are low and that most of their content is oriented towards the creation of user communities based on viral content and entertainment. However, albeit with a lesser presence, one can also identify accounts and messages that adapt their content to the specific characteristics of TikTok. Their virality and engagement figures illustrate that there is indeed a niche for current affairs on this social network.

Overview

This data set consists of links to social network items for 34 different forensic events that took place between August 14th, 2018 and January 06th, 2021. The majority of the text and images are from Twitter (a minor part is from Flickr, Facebook and Google+), and every video is from YouTube.

Data Collection

We used Social Tracker (https://github.com/MKLab-ITI/mmdemo-dockerized), along with the social medias' APIs, to gather most of the collections. For a minor part, we used Twint (https://github.com/twintproject/twint). In both cases, we provided keywords related to the event to receive the data.

It is important to mention that, in procedures like this one, usually only a small fraction of the collected data is in fact related to the event and useful for a further forensic analysis.

Content

We have data from 34 events, and for each of them we provide the files:

items_full.csv: It contains links to any social media post that was collected.

images.csv: Enlists the images collected. In some files there is a field called "ItemUrl", that refers to the social network post (e.g., a tweet) that mentions that media.

video.csv: Urls of YouTube videos that were gathered about the event.

video_tweet.csv: This file contains IDs of tweets and IDs of YouTube videos. A tweet whose ID is in this file has a video in its content. In turn, the link of a Youtube video whose ID is in this file was mentioned by at least one collected tweet. Only two collections have this file.

description.txt: Contains some standard information about the event, and possibly some comments about any specific issue related to it.

In fact, most of the collections do not have all the files above. Such an issue is due to changes in our collection procedure throughout the time of this work.

Events

We divided the events into six groups. They are,

1. Fire

• Devastating fire is the main issue of the event, therefore most of the informative pictures show flames or burned constructions

• 14 Events

2. Collapse

• Most of the relevant images depict collapsed buildings, bridges, etc. (not caused by fire).

• 5 Events

3. Shooting

• Likely images of guns and police officers. Few or no destruction of the environment.

• 5 Events

4. Demonstration

• Plethora of people on the streets. Possibly some problem took place on that, but in most cases the demonstration is the actual event.

• 7 Events

5. Collision

• Traffic collision. Pictures of damaged vehicles on an urban landscape. Possibly there are images with victims on the street.

• 1 Event

6. Flood

• Events that range from fierce rain to a tsunami. Many pictures depict water.

• 2 Events

We enlist the events in the file recod-ai-events-dataset-list.pdf

Media Content

Due to the terms of use from the social networks, we do not make publicly available the texts, images and videos that were collected. However, we can provide some extra piece of media content related to one (or more) events by contacting the authors.

Funding

DéjàVu thematic project, São Paulo Research Foundation (grants 2017/12646-3, 2018/18264-8 and 2020/02241-9)

ABSTRACT

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.

INTRODUCTION

Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.

The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).

Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).

These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.

PARTICIPANTS

The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).

DATE STRUCTURE AND ARCHIVES FORMAT

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

Social network

The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.

Personal networks

Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).

Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.

Sense of community and metropolitan displacements

The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:

 • Socio-economic data.


 • Data on habitual residence.


 • Information on intercity journeys.


 • Identity and sense of community.


 • Personal network indicators.


 • Social network indicators.

DATA ACCESS

Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.

The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: https://www.flickr.com/photos/25906481@N07/albums/72157667029974755.

In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:

Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp

The English version of this article can be downloaded from: https://tinyurl.com/yy9s2byl

CONCLUSION

The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.

The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.

ACKNOWLEDGEMENTS

The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.

REFERENCES

Brandes, U., & Wagner, D. (2004). Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), Graph Drawing Software (pp. 321-340). New York: Springer-Verlag.

Maya-Jariego, I. (2018). Why name generators with a fixed number of alters may be

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict sentiment score for news headline. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions.

Please refer to https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms for more details

Citation request
Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR

The average person has 8-9 social media accounts. This has doubled since 2013, when the average person just had 4-5 accounts.

please cite this dataset by :

Nicolas Turenne. The rumour spectrum. PLoS ONE, Public Library of Science, 2018, 13 (1), pp.e0189080.1-27. 〈10.1371/journal.pone.0189080〉. 〈hal-01691934〉

The set of these datasets are made to analyze information credibility in general (rumor and disinformation for English and French documents), and occuring on the social web. Target databases about rumor, hoax and disinformation helped to collect obviously misinformation. Some topic (with keywords) helps us to made corpora from the micrroblogging platform Twitter, great provider of rumors and disinformation.

1 corpus describes Texts from the web database about rumors and disinformation. 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French). 4 corpora from Social Media Twitter randomly built (2 in English, 2 in French). 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French).

Size of different corpora :

Social Web Rumorous corpus: 1,612

French Hollande Rumorous corpus (Twitter): 371 French Lemon Rumorous corpus (Twitter): 270 English Pin Rumorous corpus (Twitter): 679 English Swine Rumorous corpus (Twitter): 1024

French 1st Random corpus (Twitter): 1000 French 2st Random corpus (Twitter): 1000 English 3st Random corpus (Twitter): 1000 English 4st Random corpus (Twitter): 1000

French Rihanna Event corpus (Twitter): 543 English Rihanna Event corpus (Twitter): 1000 French Euro2016 Event corpus (Twitter): 1000 English Euro2016 Event corpus (Twitter): 1000

A matrix links tweets with most 50 frequent words

Text data :

_id : message id body text : string text data

Matrix data :

52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 11,102 lines (each line is a message)

Hidalgo corpus: lines range 1:75 Lemon corpus : lines range 76:467 Pin rumor : lines range 468:656 swine : lines range 657:1311

random messages : lines range 1312:11103

Sample contains : French Pin Rumorous corpus (Twitter): 679 Matrix data :

52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 189 lines (each line is a message)

The results might surprise you when looking at internet users that are active on social media in each country.

56.8% of the world’s total population is active on social media.

The cross-lingual natural disaster dataset includes public tweets collected using Twitter’s public API, filtering by location-related keywords and date, without using any additional filtering (e.g., we did not restrict the query to specific languages). We considered two disaster events and two long-term natural disasters across Europe (floods and wildfires) that received substantial news coverage internationally.

Three of the top languages were common to all the studied events: English (ISO 639-1 code: en), Spanish (es), and French (fr). Additionally, we found hundreds of messages for each event in other five languages, including Arabic (ar), German (de), Japanese (ja), Indonesian (id), Italian (it) and Portuguese (pt).

After collecting the data, we labelled tweets that contained potentially informative factual information. We name this group of tweets “informative messages.” Next, we used crowdsourcing to further categorize the messages into various informational categories. We asked three different workers to label each informative messages across languages. The target categories were based on an ontology from TREC-IS 2018, where we grouped some low level ontology categories into higher-level ones.

Full edition for scientific use. PUMA Surveys consist of separate modules designed and prepared by different principle investigators. This PUMA Survey consists of three modules: MODULE 1 "Non-Health Influences on Generic Health Ratings: Comparing the Susceptibility of Self-Rated Health (SRH) and the Minimum European Health Module (MEHM) to Biases Due to Optimism, Hypochondriasis, and Social Desirability", MODULE 2 "Online completion versus face-to-face completion. Testing mixing modes of data collection for Austrian social surveys", MODULE 3 "Concerns of Smartphone Owners When Using their Device for Research". Fieldwork was conducted by Statistics Austria. MODULE 1: Non-Health Influences on Generic Health Ratings: Comparing the Susceptibility of Self-Rated Health (SRH) and the Minimum European Health Module (MEHM) to Biases Due to Optimism, Hypochondriasis, and Social Desirability (Patrick Lazarevič, Martina Brandt, Marc Luy, Caroline Berghammer) Self-rated health (SRH) is the most widely used single-indicator of health in many scientific disciplines (Jylhä 2009). Even though more comprehensive approaches to measure generic health exist, they are often too time consuming for survey interviews, especially in multi-thematic surveys, due to time limitations. Research in this regard has shown that, even when controlling for comprehensive health information, SRH is noticeably and independently influenced by non-health factors like satisfaction with life or social participation (e.g., Lazarevič 2018). While these results illustrate that health ratings are influenced by non-health factors, the personality traits that are assumed to bias SRH (e.g., optimism, social desirability, or hypochondriasis) are typically not directly measured. The Minimum European Health Module (MEHM), as proposed by Robine & Jagger (2003), complements SRH with the questions whether the respondent suffers from a chronic disease and whether and to what extent they are limited in their usual activities due to a health problem. Thus, MEHM can be seen as a compromise between using SRH as a single-indicator and a comprehensive scale while covering the two most relevant factors for health ratings, i.e., chronic diseases and the functional status (Lazarevič 2018). While MEHM is obviously less time- and cost-intensive than more comprehensive approaches to measure health and there was some research done on its components separately (e.g., Berger et al. 2015), hardly anything is known about its usefulness as a short-scale of generic health, its overall psychometric properties, and its susceptibility to non-health factors potentially biasing the health measurement. This module tested the feasibility and utility of using the Minimum European Health Module (MEHM) as a short scale for measuring generic health. We demonstrate the feasibility of extracting a factor score from MEHM utilizing confirmatory factor analyses based on polychoric correlations. Further analyses suggest that this factor score might be useful in reducing bias in generic health measurement due to optimism and social desirability. MODULE 2: Online completion versus face-to-face completion. Testing mixing modes of data collection for Austrian social surveys (Markus Hadler, Franz Höllinger, Anja Eder) Collecting data online is a promising tool, given the problems survey research faces in terms of lowering response rates and increasing costs. Yet, the results on the comparability of online and face-to-face surveys are ambiguous (see Roberts et al. 2016). Therefore, the aim of our research is to test differences in responses when completing surveys online compared to collecting the same data face-to-face. Our PUMA-module collects some of the core ISSP questions online, which were asked face-to-face (CAPI) in the same time-period. The topics of the ISSP questionnaires 2017 and 2018 are “Social Networks” and “Religion.” At face value, we expect that these two areas may attract different respondents when conducted online as compared to face-to-face. Online networking should be more prevalent and traditional religious activities less common among the online respondents. If there are no significant differences between these two samples, our study will be a strong indicator that online tools are valid instruments. Therefore, the mixed modes design aims to break new ground in understanding the advantages and limitations, the costs and benefits of combining online and face-to-face interviews in Austria on the basis of two prominent survey modules from the International Social Survey Programme. MODULE 3: Concerns of Smartphone Owners When Using their Device for Research (Florian Keusch, Martin Weichbold) Smartphone use is on the rise worldwide (Pew Research Center 2017). Survey researchers are aware that smartphone users increasingly complete online surveys on their mobile devices and have investigated the quality of survey data provided via smartphones (e.g., Couper et al. 2017; Keusch & Yan 2017). At the same time, the rising penetration of smartphones also gives researchers the chance to collect data from smartphone users that goes beyond self-reporting through surveys. Smartphones can be used to collect a variety of data about respondents such as geolocation, measures of physical activity, online behavior and browser history, app usage, call logs, or photos (Link et al. 2014). These data would allow researchers to make inferences about, among others, users’ mobility patterns, consumer behavior, health, and social interactions. Compared to surveys, which rely on self-reports, passive mobile data collection has the potential to provide richer data (because it can be collected in much higher frequencies), to decrease respondent burden (because fewer survey questions need to be asked), and to reduce measurement error (because of reduction in recall errors and social desirability). However, agreeing to allow for passive collection of data from smartphones is an additional step in the consent process, and participants might feel uncomfortable sharing these data with researchers due to security, privacy, and confidentiality concerns. In addition, different subgroups might differ in their skills of smartphone use and thus feel more or less comfortable using smartphones for research, leading to bias due to differential nonresponse of specific groups. This module wants to find out whether new forms of smartphone data collection (using sensors, apps, and camera) could be a supplement to survey research as they provide rich data and could enlarge our knowledge about people’s behavior while reducing respondent burden. Collecting these data has ethical and practical implications: agreeing to collect data from smartphones is an additional step in the consent process, and participants might feel uncomfortable sharing these data with researchers due to security, privacy, and confidentiality concerns. In addition, different subgroups might differ in their skills of smartphone use and thus feel more or less comfortable using smartphones for research, leading to bias due to differential nonparticipation of specific groups. We find that concern for using smartphones for research differs by research task, and that the diversity of smartphone activities correlates with concern.

Gen Z and Millennials are the biggest social media users of all age groups.

Context

This dataset is a collection of datasets from different sources related to the automatic detection of cyber-bullying. The data is from different social media platforms like Kaggle, Twitter, Wikipedia Talk pages and YouTube. The data contain text and labeled as bullying or not. The data contains different types of cyber-bullying like hate speech, aggression, insults and toxicity.

Content

The data is from different social media platforms like Kaggle, Twitter, Wikipedia Talk pages and YouTube. The data contain text and labeled as bullying or not. The data contains different types of cyber-bullying like hate speech, aggression, insults and toxicity.

Acknowledgements

Elsafoury, Fatma (2020), “Cyberbullying datasets”, Mendeley Data, V1, doi: 10.17632/jf4pzyvnpj.1

The methodology used to create this data collection was content analysis. The studied population was left-wing and right-wing alternative media. The sampling procedure used was the most influential alternative media at the time (measured by sites that have the most shared articles on social media).

For left-wing sites, the sample included The Canary, The Skwawkbox, Evolve Politics, Another Angry Voice and Novara Media, and for right-wing sites, Guido Fawkes, Breitbart London, Westmonster and The Conservative Woman.

One first dataset examined 3452 items, and quantified the degree and nature of critique towards mainstream media. The second dataset examined 14,807 Tweets from the main Twitter accounts of alternative media sites. We examined three-week sample periods each year between 2015 and 2018 (6–25 October 2015; 9–29 October 2016; 30 April–7 June 2017; and 8–28 October 2018.).

The first dataset was examined in three ways.

Part 1 file- This contains content analysis data on the publication date, headline and article author, along with if content opinion- or fact-driven content, the topic, if it was about policy coverage, the party political focus and sentiment.

Part 2 file - this relates to different types of sources which could be multiple within one article

Part 3 file - this relates to the overall topic tone and tone of article.

MSM critique file - This file contains content analysis data on the degree to which mainstream media was part of routine coverage. Four areas were examined: The media entity, The media organisation, the sentiment and finally criticism or praise type.

Twitter file - tis relates to Twitter analysis. We examined the purpose of tweets, political reference and sentiment, and media reference and sentiment.

After the British public voted to leave the EU, Donald Trump's Presidential victory and Jeremy Corbyn's rising support during the 2017 UK election campaign, the mainstream media (MSM) were criticised for not anticipating these events. They were accused of not reflecting or understanding many people's alienation from and anger with the mainstream media and political establishment. On both sides of the political spectrum, the acronym MSM has become a widely used pejorative term to characterise a broad range of legacy media that represents the establishment, protecting the interests of elites and perpetuating the political status quo.

It is in this context that many voters went beyond the during the 2017 UK general election campaign and turned to what have been labelled alt-left media sites, where more pro-Labour and anti-MSM messages were being conveyed. This included sites such as The Canary, Evolve Politics, Wings over Scotland, Novara Media, Skwawkbox and Another Angry Voice. They became a prominent part of the campaign because they reached voters across many social media platforms, particularly Facebook, and bypassed the reliance on MSM for news.

The aim of this research project was to understand the content of both left- and right-wing alternative online political media, such as Westmonster, Breitbart UK, Conservative Woman and Guido Fawkes. We will examine the content alternative media by carrying out a comprehensive analysis of selected sites and their use of twitter between 2017 and 2021.

Analysis of ‘Police Killings US’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/azizozmen/police-killings-us on 13 February 2022.

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

"In 2015, The Washington Post began to log every fatal shooting by an on-duty police officer in the United States. In that time there have been more than 5,000 such shootings recorded by The Post. After Michael Brown, an unarmed Black man, was killed in 2014 by police in Ferguson, Mo., a Post investigation found that the FBI undercounted fatal police shootings by more than half. This is because reporting by police departments is voluntary and many departments fail to do so. The Washington Post’s data relies primarily on news accounts, social media postings, and police reports. Analysis of more than five years of data reveals that the number and circumstances of fatal shootings and the overall demographics of the victims have remained relatively constant..." SOURCE ==> Washington Post Article

For more information about this story

This dataset has been prepared by The Washington Post (they keep updating it on runtime) with every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015.

2016 PoliceKillingUS DATASET
2017 PoliceKillingUS DATASET
2018 PoliceKillingUS DATASET
2019 PoliceKillingUS DATASET
2020 PoliceKillingUS DATASET

Features at the Dataset:

The file fatal-police-shootings-data.csv contains data about each fatal shooting in CSV format. The file can be downloaded at this URL. Each row has the following variables:

• id: a unique identifier for each victim
• name: the name of the victim
• date: the date of the fatal shooting in YYYY-MM-DD format
• manner_of_death: shot, shot and Tasered
• armed: indicates that the victim was armed with some sort of implement that a police officer believed could inflict harm
  • undetermined: it is not known whether or not the victim had a weapon
  • unknown: the victim was armed, but it is not known what the object was
  • unarmed: the victim was not armed
• age: the age of the victim
• gender: the gender of the victim. The Post identifies victims by the gender they identify with if reports indicate that it differs from their biological sex.
  • M: Male
  • F: Female
  • None: unknown
• race:
  • W: White, non-Hispanic
  • B: Black, non-Hispanic
  • A: Asian
  • N: Native American
  • H: Hispanic
  • O: Other
  • None: unknown
• city: the municipality where the fatal shooting took place. Note that in some cases this field may contain a county name if a more specific municipality is unavailable or unknown.
• state: two-letter postal code abbreviation
• signs of mental illness: News reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.
• threat_level: The threat_level column was used to flag incidents for the story by Amy Brittain in October 2015. http://www.washingtonpost.com/sf/investigative/2015/10/24/on-duty-under-fire/ As described in the story, the general criteria for the attack label was that there was the most direct and immediate threat to life. That would include incidents where officers or others were shot at, threatened with a gun, attacked with other weapons or physical force, etc. The attack category is meant to flag the highest level of threat. The other and undetermined categories represent all remaining cases. Other includes many incidents where officers or others faced significant threats.
• flee: News reports have indicated the victim was moving away from officers
  • Foot
  • Car
  • Not fleeing

The threat column and the fleeing column are not necessarily related. For example, there is an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer. Also, attacks represent a status immediately before fatal shots by police while fleeing could begin slightly earlier and involve a chase. - body_camera: News reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.

SOURCE

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

Overview Welcome to Kaggle's third annual Machine Learning and Data Science Survey ― and our second-ever survey data challenge. You can read our executive summary here.

This year, as in 2017 and 2018, we set out to conduct an industry-wide survey that presents a truly comprehensive view of the state of data science and machine learning. The survey was live for three weeks in October, and after cleaning the data we finished with 19,717 responses!

There's a lot to explore here. The results include raw numbers about who is working with data, what’s happening with machine learning in different industries, and the best ways for new data scientists to break into the field. We've published the data in as raw a format as possible without compromising anonymization, which makes it an unusual example of a survey dataset.

Challenge This year Kaggle is launching the second annual Data Science Survey Challenge, where we will be awarding a prize pool of $30,000 to notebook authors who tell a rich story about a subset of the data science and machine learning community.

In our third year running this survey, we were once again awed by the global, diverse, and dynamic nature of the data science and machine learning industry. This survey data EDA provides an overview of the industry on an aggregate scale, but it also leaves us wanting to know more about the many specific communities comprised within the survey. For that reason, we’re inviting the Kaggle community to dive deep into the survey datasets and help us tell the diverse stories of data scientists from around the world.

The challenge objective: tell a data story about a subset of the data science community represented in this survey, through a combination of both narrative text and data exploration. A “story” could be defined any number of ways, and that’s deliberate. The challenge is to deeply explore (through data) the impact, priorities, or concerns of a specific group of data science and machine learning practitioners. That group can be defined in the macro (for example: anyone who does most of their coding in Python) or the micro (for example: female data science students studying machine learning in masters programs). This is an opportunity to be creative and tell the story of a community you identify with or are passionate about!

Submissions will be evaluated on the following:

Composition - Is there a clear narrative thread to the story that’s articulated and supported by data? The subject should be well defined, well researched, and well supported through the use of data and visualizations. Originality - Does the reader learn something new through this submission? Or is the reader challenged to think about something in a new way? A great entry will be informative, thought provoking, and fresh all at the same time. Documentation - Are your code, and notebook, and additional data sources well documented so a reader can understand what you did? Are your sources clearly cited? A high quality analysis should be concise and clear at each step so the rationale is easy to follow and the process is reproducible To be valid, a submission must be contained in one notebook, made public on or before the submission deadline. Participants are free to use any datasets in addition to the Kaggle Data Science survey, but those datasets must also be publicly available on Kaggle by the deadline for a submission to be valid.

How to Participate To make a submission, complete the submission form. Only one submission will be judged per participant, so if you make multiple submissions we will review the last (most recent) entry.

No submission is necessary for the Weekly Notebook Award. To be eligible, a notebook must be public and use the 2019 Data Science Survey as a data source.

Submission deadline: 11:59PM UTC, December 2nd, 2019.

Survey Methodology This survey received 19,717 usable respondents from 171 countries and territories. If a country or territory received less than 50 respondents, we grouped them into a group named “Other” for anonymity.

We excluded respondents who were flagged by our survey system as “Spam”.

Most of our respondents were found primarily through Kaggle channels, like our email list, discussion forums and social media channels.

The survey was live from October 8th to October 28th. We allowed respondents to complete the survey at any time during that window. The median response time for those who participated in the survey was approximately 10 minutes.

Not every question was shown to every respondent. You can learn more about the different segments we used in the survey_schema.csv file. In general, respondents with more experience were asked more questions and respondents with less experience were asked less questions.

To protect the respondents’ identity, the answers to multiple choice questions have been separated into a separate data file from the open-ended responses. We do not provide a key to match up the multiple choice and free form responses. Further, the free form responses have been randomized column-wise such that the responses that appear on the same row did not necessarily come from the same survey-taker.

Multiple choice single response questions fit into individual columns whereas multiple choice multiple response questions were split into multiple columns. Text responses were encoded to protect user privacy and countries with fewer than 50 respondents were grouped into the category "other".

Data has been released under a CC 2.0 license: https://creativecommons.org/licenses/by/2.0/

Regional use of social media has a significant effect on the male and female social media statistics.