The number of snapchat users in the United States was forecast to continuously increase between 2024 and 2028 by in total 5.7 million users (+5.3 percent). After the ninth consecutive increasing year, the snapchat user base is estimated to reach 113.3 million users and therefore a new peak in 2028. Notably, the number of snapchat users of was continuously increasing over the past years.The user numbers, depicted here regarding the platform Snapchat, 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.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 snapchat users in countries like Canada and Mexico.

The global number of snapchat users in was forecast to continuously increase between 2024 and 2028 by in total 165.7 million users (+27 percent). After the ninth consecutive increasing year, the snapchat user base is estimated to reach 779.3 million users and therefore a new peak in 2028. Notably, the number of snapchat users of was continuously increasing over the past years.The user numbers, depicted here regarding the platform Snapchat, 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.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 snapchat users in countries like Africa and the Americas.

As of the first quarter of 2025, photo and video sharing app Snapchat had 460 million daily active users worldwide, up from 460 million global DAU in the fourth quarter of 2024. The app has seen steady increases in daily active users since the beginning of 2019. Snapchat is relevant for teenagers Originally launched in 2011, Snapchat has become one of the most popular social messaging and photo sharing apps worldwide; making its CEO and co-founder Evan Spiegel one of the world’s richest social media entrepreneurs. With almost 800 million active users as of April 2024, Snapchat easily ranks among the most popular social networks worldwide. According to U.S. teenagers in fall 2023, Snapchat is the second most important social network of their generation, ahead of photo sharing competitor Instagram and other networks such as Twitter or Facebook. Overall, 48 percent of U.S. internet users aged 15 to 25 years were reportedly using Snapchat, the highest usage reach among any age group. When it comes to user satisfaction with social media, Snapchat’s performance is fair to middling. According to recent survey data, the social app scored 72 out of 100 points on a consumer satisfaction scale, ranking ahead of Twitter and Facebook but behind Pinterest and eternal rival Instagram.

The SNAP participation rate shows how many households in Champaign County receive SNAP benefits, as a percentage of the total number of households in the county. The SNAP participation rate can serve as an indicator of poverty and need in the area, as income-based thresholds establish SNAP eligibility. However, not every household in poverty receives SNAP benefits, as can be determined by comparing the poverty rate between 2005 and 2023 and the percentage of households receiving SNAP benefits between 2005 and 2023.

The number of households and the percentage of households receiving SNAP benefits was higher in 2023 than in 2005, but we cannot establish a trend based on year-to-year changes, as in many years these changes are not statistically significant.

SNAP participation data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Receipt of Food Stamps/SNAP in the Past 12 Months by Presence of Children Under 18 Years for Households.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (26 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (5 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

soc-RedditHyperlinks: Social Network: Reddit Hyperlink Network

The hyperlink network represents the directed connections between two subreddits (a subreddit is a community on Reddit). We also provide subreddit embeddings. The network is extracted from publicly available Reddit data of 2.5 years from Jan 2014 to April 2017.

Subreddit Hyperlink Network: the subreddit-to-subreddit hyperlink network is extracted from the posts that create hyperlinks from one subreddit to another. We say a hyperlink originates from a post in the source community and links to a post in the target community. Each hyperlink is annotated with three properties: the timestamp, the sentiment of the source community post towards the target community post, and the text property vector of the source post. The network is directed, signed, temporal, and attributed.

Note that each post has a title and a body. The hyperlink can be present in either the title of the post or in the body. Therefore, we provide one network file for each.

Subreddit Embeddings: We have also provided embedding vectors representing each subreddit. These can be found in this dataset link: subreddit embedding dataset. Please note that some subreddit embeddings could not be generated, so this file has 51,278 embeddings.

soc-sign-bitcoin-otc: Bitcoin OTC trust weighted signed network

This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin OTC. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin OTC rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

soc-sign-bitcoin-alpha: Bitcoin Alpha trust weighted signed network

This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin Alpha. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin Alpha rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

soc-sign-epinions: Epinions social network

This is who-trust-whom online social network of a a general consumer review site Epinions.com. Members of the site can decide whether to ''trust'' each other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user.

wiki-Elec: Wikipedia adminship election data

Wikipedia is a free encyclopedia written collaboratively by volunteers around the world. A small part of Wikipedia contributors are administrators, who are users with access to additional technical features that aid in maintenance. In order for a user to become an administrator a Request for adminship (RfA) is issued and the Wikipedia community via a public discussion or a vote decides who to promote to adminship. Using the latest complete dump of Wikipedia page edit history (from January 3 2008) we extracted all administrator elections and vote history data. This gave us nearly 2,800 elections with around 100,000 total votes and about 7,000 users participating in the elections (either casting a vote or being voted on). Out of these 1,200 elections resulted in a successful promotion, while about 1,500 elections did not result in the promotion. About half of the votes in the dataset are by existing admins, while the other half comes from ordinary Wikipedia users.

Dataset has the following format:

• E: did the elector result in promotion (1) or not (0)
• T: time election was closed
• U: user id (and screen name) of editor that is being considered for promotion
• N: user id (and screen name) of the nominator
• V: vote(1:support, 0:neutral, -1:oppose) user_id time screen_name

wiki-RfA: Wikipedia Requests for Adminship (with text)

For a Wikipedia editor to become an administrator, a request for adminship (RfA) must be submitted, either by the candidate or by another community member. Subsequently, any Wikipedia member may cast a supporting, neutral, or opposing vote.

We crawled and parsed all votes since the adoption of the RfA process in 2003 through May 2013. The dataset contains 11,381 users (voters and votees) forming 189,004 distinct voter/votee pairs, for a total of 198,275 votes (this is larger than the number of distinct voter/votee pairs because, if the same user ran for election several times, the same voter/votee pair may contribute several votes).

This induces a directed, signed network in which nodes represent Wikipedia members and edges represent votes. In this sense, the...

Introduction These three datasets are used in the analysis of human mobility research paper [1]. For each dataset, there are checkins info and friendshio info, Brightkite: "brightkite_checkins.csv" and "brightkite_friends.csv". Gowalla: "gowalla_checkins.csv" and "gowalla_friends.csv". Weeplaces: "weeplace_checkins.csv" and "weeplace_friends.csv" Basic Description BrightKite [2] is a LBSN service provider that allowed registered users to connect with their existing social ties and also meet new people based on the places that they go. Once a user "checked in" at a place, they could post notes and photos to a location and other users could comment on those posts. The social relationship network was collected using their public API. The raw dataset is from SNAP https://snap.stanford.edu/data/loc-brightkite.html. Gowalla [2] is a LBSN website where users share their locations by checking-in. In early versions of the service, users would occasionally receive a virtual "Item" as a bonus upon checking in, and these items could be swapped or dropped at other spots. Users became "Founders" of a spot by dropping an item there. This incentivises users to create new check-ins, not necessarily to check-in consistently at frequently visited locations. The social relationship network is undirected and was collected using their public API. The raw dataset is from SNAP https://snap.stanford.edu/data/loc-gowalla.html. Weeplaces --This is collected from Weeplaces and integrated with the APIs of other LBSN services, e.g., Facebook Places, Foursquare, and Gowalla. Users can login Weeplaces using their LBSN accounts and connect with their social ties in the same LBSN who have also used this application. Weeplaces visualizes your check-ins on a map. Unlike Gowalla, there is no direct incentive in Weeplaces to alter one's visitation habits or check-ins, so there should be a more accurate representation of a regular person's mobility patterns. The raw dataset is from the website https://www.yongliu.org/datasets/. More details can be found in the data description of paper [1]. Reference [1] Chen, Z., Kelty, S., Welles, B.F., Bagrow, J.P., Menezes, R. and Ghoshal, G., 2021. Contrasting social and non-social sources of predictability in human mobility. arXiv preprint arXiv:2104.13282. [2] Cho, Eunjoon, Seth A. Myers, and Jure Leskovec. "Friendship and mobility: user movement in location-based social networks." In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1082-1090. 2011. {"references": ["Chen, Z., Kelty, S., Welles, B.F., Bagrow, J.P., Menezes, R. and Ghoshal, G., 2021. Contrasting social and non-social sources of predictability in human mobility.\u00a0arXiv preprint arXiv:2104.13282.", "Cho, Eunjoon, Seth A. Myers, and Jure Leskovec. "Friendship and mobility: user movement in location-based social networks." In\u00a0Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1082-1090. 2011."]}

The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload. The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual. This dataset includes separate SNAP QC files for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months. Resources in this dataset:Resource Title: Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database (Period 2). File Name: qc_pub_fy2020_per2.csvResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.Resource Title: Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database (Period 1). File Name: qc_pub_fy2020_per1.csvResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.Resource Title: Technical Documentation for the Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database and the QC Minimodel. File Name: FY2020TechDoc.pdfResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.

Cash Assistance and SNAP case reopenings with missed benefits ordered by NYS Welfare Management System (WMS) closing reason code. The total number of instances during the specified quarter and year in which a Cash Assistance case was terminated and at least one disbursement date passed before such case was reopened for the same type of ongoing Cash Assistance or SNAP. This data is ordered by the NYS Welfare Management System (WMS) closing reason code that was used to close the case prior to the case reopening. Each record is one case which may include one or many recipients. NOTE: Because asterisks represent values between 1 and 10, users should not sum values across columns or rows to determine the total number of cases within a quarter. Accurate totals are represented in the rows and columns labeled “Total”.

Trends in Supplemental Nutrition Assistance Program Participation Rates: Fiscal Year 2010 to Fiscal Year 2017 is the latest in a series on SNAP participation rates, which estimate the proportion of people eligible for benefits under Federal income and asset rules who actually participate in the program. This report presents rates for fiscal year (FY) 2017, comparing them to rates for FYs 2010 through 2016.

A tutorial to quantify BAM earthquake using SNAP

Differential InSAR is a satellite-based remote sensing technique that can be used to quantify small displacements of the Earth's surface. This is due to the interferometric phase being much more sensitive to the ground motion than to the elevation difference. This practical session will explain how to apply it to real-world Envisat ASAR images, with user-oriented open-source SNAP software. The main goal is able to generate ground motion from a pair of SAR images to map the Earthquake of 2003 in BAM city.

More information can be found here:

Video: https://youtu.be/Uc-5F9Vz04w

https://github.com/BAMInSAR

https://www.facebook.com/groups/RadarInterferometry

Die Studie über Facebook-Nutzer wurde von infratest dimap im Auftrag der Konrad-Adenauer-Stiftung durchgeführt. Im Erhebungszeitraum 26. November bis 4. Dezember 2018 wurden 2.041 Facebook-Nutzer in Onlineinterviews (CAWI) zu folgenden Themen befragt: Internetnutzung, Facebook-Gruppen, Facebooknutzung, politische Inhalte auf Facebook, Reaktion auf Inhalte, Bildexperiment und Sonntagsfrage. Die Auswahl der Befragten erfolgte durch eine Quotenstichprobe aus einem Online-Access-Panel. Nutzung verschiedener Internetangebote (Tinder, Facebook, Twitter, snapchat, Instagram, YouTube, Online-Zeitungen, nichts davon); Nutzung offener oder geschlossener Facebook-Gruppen; Facebook-Inhalte zu politischen Themen, zu berufsbezogenen Themen, zu Hobbies, zur Unterhaltung bzw. zu anderen Themen; Art der Facebook-Nutzung (lese/ like/ teile Inhalte, schreibe Kommentare, verbreite eigene Inhalte); politische Facebook-Nutzung (lese/ like/ teile politische Inhalte, schreibe Kommentare zu politischen Themen, verbreite eigenen Inhalt zu politischen Themen); Reaktion auf Facebook- Inhalte bzw. Kommentare (fühle mich informiert, unterhalten, verärgert, provoziert); Zustimmung zu verschiedenen Aussagen zu Facebook (auf Facebook regen mich andere auf, zeige ich anderen ihre Grenzen, kann ich anonym meine Meinung sagen, finde ich viele verschiedene Meinungen, finde ich Meinungen, die sonst unterdrückt werden, traue ich mich Dinge zu sagen/ teilen, die ich sonst nicht sagen würde); Parteipräferenz (Sonntagsfrage); Kommentar (offen) zu einem provozierenden Bild (Split A: Flüchtlinge, Split B: Pegida). Demographie: Geschlecht; Alter (Geburtsjahr); Bildung; Erwerbstätigkeit; berufliche Stellung; Haushaltsnettoeinkommen (gruppiert); Bundesland. Zusätzlich verkodet wurde: lfd. Nummer; Gewichtungsfaktor. The study on Facebook users was conducted by infratest dimap on behalf of the Konrad Adenauer Foundation. During the survey period from November 26 to December 4, 2018, 2,041 Facebook users were surveyed in online interviews (CAWI) on the following topics: internet use, Facebook groups, Facebook use, political content on Facebook, reaction to content, image experiment and Sunday question. Respondents were selected by quota sampling from an online access panel. Use of various internet services (Tinder, Facebook, Twitter, snapchat, Instagram, YouTube, online newspapers, none of the above); use of open or closed Facebook groups; Facebook content on political topics, on job-related topics, on hobbies, on entertainment or on other topics; type of Facebook use (read/ like/ share content, write comments, disseminate own content); political Facebook use (read/ like/ share political content, write comments on political topics, disseminate own content on political topics); reaction to Facebook content or comments (do I feel informed, entertained, annoyed, provoked, etc.). comments (feel informed, entertained, annoyed, provoked); agreement with various statements on Facebook (on Facebook others upset me, I show others their limits, I can speak my mind anonymously, I find many different opinions, I find opinions that are otherwise suppressed, I dare to say/ share things I would not otherwise say); party preference (Sunday question); comment (open) on a provocative image (split A: refugees, split B: Pegida). Demography: sex; age (year of birth); education; employment; occupational status; net household income (grouped); federal state. Additionally coded: serial number; weighting factor.

Youtube social network and ground-truth communities

https://snap.stanford.edu/data/com-Youtube.html

Dataset information

Youtube (http://www.youtube.com/) is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider
such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.
(http://socialnetworks.mpi-sws.org/data-imc2007.html)

We regard each connected component in a group as a separate ground-truth
community. We remove the ground-truth communities which have less than 3
nodes. We also provide the top 5,000 communities with highest quality
which are described in our paper (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Network statistics
Nodes 1,134,890
Edges 2,987,624
Nodes in largest WCC 1134890 (1.000)
Edges in largest WCC 2987624 (1.000)
Nodes in largest SCC 1134890 (1.000)
Edges in largest SCC 2987624 (1.000)
Average clustering coefficient 0.0808
Number of triangles 3056386
Fraction of closed triangles 0.002081
Diameter (longest shortest path) 20
90-percentile effective diameter 6.5
Community statistics
Number of communities 8,385
Average community size 13.50
Average membership size 0.10

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-youtube.ungraph.txt.gz Undirected Youtube network
com-youtube.all.cmty.txt.gz Youtube communities
com-youtube.top5000.cmty.txt.gz Youtube communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 1-based, with nodes numbered 1 to
1,157,827.

In the SuiteSparse Matrix Collection, Problem.A is the undirected Youtube
network, a matrix of size n-by-n with n=1,134,890, which is the number of
unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (1-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 16,386
which represents the communities in the com-youtube.all.cmty.txt file.
The kth line in that file defines the kth community, and is the column
C(:,k), where C(i,k)=1 if person ...

Context

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

Contents

• Reviews.csv: Pulled from the corresponding SQLite table named Reviews in database.sqlite
  
• database.sqlite: Contains the table 'Reviews'
  
  

Data includes:
- Reviews from Oct 1999 - Oct 2012
- 568,454 reviews
- 256,059 users
- 74,258 products
- 260 users with > 50 reviews

Acknowledgements

See this SQLite query for a quick sample of the dataset.

If you publish articles based on this dataset, please cite the following paper:

• J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW, 2013.

Deezer Ego Nets

The ego-nets of Eastern European users collected from the music streaming service Deezer in February 2020. Nodes are users and edges are mutual follower relationships. The related task is the prediction of gender for the ego node in the graph.

Github Stargazers

The social networks of developers who starred popular machine learning and web development repositories (with at least 10 stars) until 2019 August. Nodes are users and links are follower relationships. The task is to decide whether a social network belongs to web or machine learning developers. We only included the largest component (at least with 10 users) of graphs.

Reddit Threads

Discussion and non-discussion based threads from Reddit which we collected in May 2018. Nodes are Reddit users who participate in a discussion and links are replies between them. The task is to predict whether a thread is discussion based or not (binary classification).

Twitch Ego Nets

The ego-nets of Twitch users who participated in the partnership program in April 2018. Nodes are users and links are friendships. The binary classification task is to predict using the ego-net whether the ego user plays a single or multple games. Players who play a single game usually have a more dense ego-net.

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#disjointgraphs

Cash Assistance and SNAP case reopenings ordered by NYS Welfare Management System (WMS) closing reason code. The total number of instances during the specified quarter and year in which a case was reopened within three months of ongoing Cash Assistance or SNAP termination and was reopened for the same type of ongoing cash assistance or SNAP. This data is ordered by the NYS Welfare Management System (WMS) closing reason code that was used to close the case prior to the case reopening. Each record is one case which may include one or many recipients. NOTE: Because asterisks represent values between 1 and 10, users should not sum values across columns or rows to determine the total number of cases within a quarter. Accurate totals are represented in the rows and columns labeled “Total”.

Ask Ubuntu temporal network

https://snap.stanford.edu/data/sx-askubuntu.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Ask Ubuntu (http://askubuntu.com/). There are three different types of
interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph sx-askubuntu-a2q) user u commented on user v's question at time t (in the graph
sx-askubuntu-c2q) user u commented on user v's answer at time t (in the
graph sx-askubuntu-c2a)

The graph sx-askubuntu contains the union of these graphs. These graphs
were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-askubuntu)
Nodes 159,316
Temporal Edges 964,437
Edges in static graph 596,933
Time span 2613 days

Dataset statistics (sx-askubuntu-a2q)
Nodes 137,517
Temporal Edges 280,102
Edges in static graph 262,106
Time span 2613 days

Dataset statistics (sx-askubuntu-c2q)
Nodes 79,155
Temporal Edges 327,513
Edges in static graph 198,852
Time span 2047 days

Dataset statistics (sx-askubuntu-c2a)
Nodes 75,555
Temporal Edges 356,822
Edges in static graph 178,210
Time span 2418 days

Source (citation)
Ashwin Paranjape, Austin R. Benson, and Jure Leskovec. "Motifs in Temporal Networks." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017.

Files
File Description
sx-askubuntu.txt.gz All interactions
sx-askubuntu-a2q.txt.gz Answers to questions
sx-askubuntu-c2q.txt.gz Comments to questions
sx-askubuntu-c2a.txt.gz Comments to answers

Data format

SRC DST UNIXTS                             

where edges are separated by a new line and

SRC: id of the source node (a user)                  
TGT: id of the target node (a user)                  
UNIXTS: Unix timestamp (seconds since the epoch)            

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The SNAP graph is 1-based, with nodes in all graphs numbered 1 to
n=515,280.

In the SuiteSparse Matrix Collection, the primary matrix, Problem.A, is
the overall static graph, with 596,993 edges, of size n-by-n with
n=159,316. These edges represent the 964,437 temporal edges. A(i,j) is
the number of times person u=nodeid(i) interacted with person v=nodeid(j), with a temporal edge (u,v,t), with any kind of interaction.
Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set.

A2Q = Problem.aux.Q2A is the static sx-askubuntu-a2q graph.
C2Q = Problem.aux.C2Q is the static sx-askubuntu-c2q graph.
C2A = Problem.aux.C2A is the static sx-askubuntu-c2a graph.
These sum together to give the the overall graph. That is,
A = A2Q + C2Q + C2A.

A2Q(u,v) is the number of times person u answered v's questions.
C2Q(u,v) is the number of times person u commented on v's question.
C2A(u,v) is the number of times person u commented on v's answer.

The temporal edges are held in:
Problem.aux.temporal_edges: [964437x3]
Problem.aux.temporal_edges_a2q: [280102x3]
Problem.aux.temporal_edges_c2q: [327513x3]
Problem.aux.temporal_edges_c2a: [356822x3]

Each row in these matrices is a single temporal edge, (u,v,t). Summing up all entries in A gives 964,437, and likewise the sum of entries in the
other graphs gives the number of temporal edges they represent.

Math Overflow temporal network

https://snap.stanford.edu/data/sx-mathoverflow.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Math Overflow (http://mathoverflow.net/). There are three different types
of interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph
sx-mathoverflow-a2q) user u commented on user v's question at time t (in
the graph sx-mathoverflow-c2q) user u commented on user v's answer at time t (in the graph sx-mathoverflow-c2a)

The graph sx-mathoverflow contains the union of these graphs. These graphs were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-mathoverflow)
Nodes 24,818
Temporal Edges 506,550
Edges in static graph 239,978
Time span 2350 days

Dataset statistics (sx-mathoverflow-a2q)
Nodes 21,688
Temporal Edges 107,581
Edges in static graph 90,489
Time span 2350 days

Dataset statistics (sx-mathoverflow-c2q)
Nodes 16,836

These data are monthly listings of households, recipients and expenditures for the Supplemental Nutrition Assistance Program.

Bitcoin Alpha trust weighted signed network

https://snap.stanford.edu/data/soc-sign-bitcoin-alpha.html

Dataset information

This is who-trusts-whom network of people who trade using Bitcoin on a
platform called Bitcoin Alpha (http://www.btcalpha.com/). Since Bitcoin
users are anonymous, there is a need to maintain a record of users'
reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin Alpha rate other members in a scale of -10 (total distrust) to
+10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

Dataset statistics
Nodes 3,783
Edges 24,186
Range of edge weight -10 to +10
Percentage of positive edges 93%

Similar network from another Bitcoin platform, Bitcoin OTC, is available at https://snap.stanford.edu/data/soc-sign-bitcoinotc.html (and as
SNAP/bitcoin-otc in the SuiteSparse Matrix Collection).

Source (citation) Please cite the following paper if you use this dataset: S. Kumar, F. Spezzano, V.S. Subrahmanian, C. Faloutsos. Edge Weight
Prediction in Weighted Signed Networks. IEEE International Conference on
Data Mining (ICDM), 2016.
http://cs.stanford.edu/~srijan/pubs/wsn-icdm16.pdf

The following BibTeX citation can be used:
@inproceedings{kumar2016edge,
title={Edge weight prediction in weighted signed networks},
author={Kumar, Srijan and Spezzano, Francesca and
Subrahmanian, VS and Faloutsos, Christos},
booktitle={Data Mining (ICDM), 2016 IEEE 16th Intl. Conf. on},
pages={221--230},
year={2016},
organization={IEEE}
}

The project webpage for this paper, along with its code to calculate two
signed network metrics---fairness and goodness---is available at
http://cs.umd.edu/~srijan/wsn/

Files
File Description
soc-sign-bitcoinalpha.csv.gz
Weighted Signed Directed Bitcoin Alpha web of trust network

Data format
Each line has one rating with the following format:

SOURCE, TARGET, RATING, TIME                      

where

SOURCE: node id of source, i.e., rater                 
TARGET: node id of target, i.e., ratee                 
RATING: the source's rating for the target,              
    ranging from -10 to +10 in steps of 1             
TIME: the time of the rating, measured as seconds since Epoch.     

Notes on inclusion into the Suite...

email-EuAll: EU email communication network

The network was generated using email data from a large European research institution. For a period from October 2003 to May 2005 (18 months) we have anonymized information about all incoming and outgoing email of the research institution. For each sent or received email message we know the time, the sender and the recipient of the email. Overall we have 3,038,531 emails between 287,755 different email addresses. Note that we have a complete email graph for only 1,258 email addresses that come from the research institution. Furthermore, there are 34,203 email addresses that both sent and received email within the span of our dataset. All other email addresses are either non-existing, mistyped or spam.

Given a set of email messages, each node corresponds to an email address. We create a directed edge between nodes i and j, if i sent at least one message to j.

email-Enron: Enron email network

Enron email communication network covers all the email communication within a dataset of around half million emails. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation. Nodes of the network are email addresses and if an address i sent at least one email to address j, the graph contains an undirected edge from i to j. Note that non-Enron email addresses act as sinks and sources in the network as we only observe their communication with the Enron email addresses.

The Enron email data was originally released by William Cohen at CMU.

wiki-Talk: Wikipedia Talk network

Wikipedia is a free encyclopedia written collaboratively by volunteers around the world. Each registered user has a talk page, that she and other users can edit in order to communicate and discuss updates to various articles on Wikipedia. Using the latest complete dump of Wikipedia page edit history (from January 3 2008) we extracted all user talk page changes and created a network.

The network contains all the users and discussion from the inception of Wikipedia till January 2008. Nodes in the network represent Wikipedia users and a directed edge from node i to node j represents that user i at least once edited a talk page of user j.

comm-f2f-Resistance: Dynamic Face-to-Face Interaction Networks

The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.

The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#email