Twitter Friends and hashtags

Context

This datasets is an extract of a wider database aimed at collecting Twitter user's friends (other accound one follows). The global goal is to study user's interest thru who they follow and connection to the hashtag they've used.

Content

It's a list of Twitter user's informations. In the JSON format one twitter user is stored in one object of this more that 40.000 objects list. Each object holds :

• avatar : URL to the profile picture

• followerCount : the number of followers of this user

• friendsCount : the number of people following this user.

• friendName : stores the @name (without the '@') of the user (beware this name can be changed by the user)

• id : user ID, this number can not change (you can retrieve screen name with this service : https://tweeterid.com/)

• friends : the list of IDs the user follows (data stored is IDs of users followed by this user)

• lang : the language declared by the user (in this dataset there is only "en" (english))

• lastSeen : the time stamp of the date when this user have post his last tweet.

• tags : the hashtags (whith or without #) used by the user. It's the "trending topic" the user tweeted about.

• tweetID : Id of the last tweet posted by this user.

You also have the CSV format which uses the same naming convention.

These users are selected because they tweeted on Twitter trending topics, I've selected users that have at least 100 followers and following at least 100 other account (in order to filter out spam and non-informative/empty accounts).

Acknowledgements

This data set is build by Hubert Wassner (me) using the Twitter public API. More data can be obtained on request (hubert.wassner AT gmail.com), at this time I've collected over 5 milions in different languages. Some more information can be found here (in french only) : http://wassner.blogspot.fr/2016/06/recuperer-des-profils-twitter-par.html

Past Research

No public research have been done (until now) on this dataset. I made a private application which is described here : http://wassner.blogspot.fr/2016/09/twitter-profiling.html (in French) which uses the full dataset (Millions of full profiles).

Inspiration

On can analyse a lot of stuff with this datasets :

• stats about followers & followings
• manyfold learning or unsupervised learning from friend list
• hashtag prediction from friend list

Contact

Feel free to ask any question (or help request) via Twitter : @hwassner

Enjoy! ;)

A complete list of live websites using the Twitter Friends technology, compiled through global website indexing conducted by WebTechSurvey.

Dataset

This dataset was created by lixysc

Released under Apache 2.0

Contents

Die Grafik zeigt eine prozentuale Verteilung der Freundesanzahl von Twitter-Accounts. Accounts mit 6 bis 10 Friends machen einen Anteil von 8,7 Prozent aus.

Dataset

This dataset was created by jettzfc

Released under Apache 2.0

Contents

The number of X (Twitter) followers of the Major League Baseball team New York Mets increased considerably from September 2011 to November 2024. In the last recorded month, the team's social media account had around 1.27 million followers.

Right now we see that depression is one of the most common problems in our society. Most of the time people are committed suicide only cause of depression. And till now there is no proper lab test way for detecting depression. Generally, doctors are detecting depression by asking some knowledge-base questions. On the other hand, there are a good number of people using social media platforms right now, where they are sharing their daily experiences, emotion, and other activity with their friends. Twitter is one of the common social platforms and also popular for data collection. I was collecting these datasets from twitter based on some depressive words. I hope that this twitter datasets will help researchers to detect depression more precisely.

This statistic provides information on the most popular luxury brands on Twitter, ranked by number of followers. In 2020, the French luxury brand Chanel was ranked first with 13 million Twitter followers, followed by Burberry, Dior, and Louis Vuitton with 8 million followers each.

Abstract (our paper)

Why does Smith follow Johnson on Twitter? In most cases, the reason why users follow other users is unavailable. In this work, we answer this question by proposing TagF, which analyzes the who-follows-whom network (matrix) and the who-tags-whom network (tensor) simultaneously. Concretely, our method decomposes a coupled tensor constructed from these matrix and tensor. The experimental results on million-scale Twitter networks show that TagF uncovers different, but explainable reasons why users follow other users.

Data

coupled_tensor: The first column is the source user id (from user id), the second column is the destination user id (to user id), and the third column is the tag id.

users.id: The first column is the user id for coupled_tensor, and the second column is the user id on Twitter.

tags.id: The first column is the tag id for coupled_tensor, and the second column is the tag (i.e. slug or list name) on Twitter. On the tags, ###follow### and ###friend### are special tags expressing follower and following.

Publication

This dataset was created for our study. If you make use of this dataset, please cite: Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa. Why Do You Follow Him? Multilinear Analysis on Twitter. Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). pp.137-138, 2015. http://doi.org/10.1145/2740908.2742715

Code

Our code outputting experiment results made available at: https://github.com/yamaguchiyuto/tagf

Note

If you would like to use larger dataset, the dataset on 1 million seed users made available at: http://dx.doi.org/10.5281/zenodo.16267 (The dataset on 0.1 million seed users is not subset of the dataset on 1 million seed users.)

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed friends of friends" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category ofdigital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarisation, misinformation, extreme speech, political discourse etc.

Explanation/Overview: This is the dataset for the analyses and results on Twitter Ego-Networks of two CS-related accounts (@EuCitSci & @SciStarter). The username have been anonymised. Purpose: The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable. As such, it does not represent raw data, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R or any other network visualisation software (e.g., Gephi). The edges represent the follow relation and were retrieved using the Twitter API. All usernames except those of the two ego-accounts were anonymised by assigning a random number to each node. Due to the size of the network, we do not include any .gexf or .gml files in this upload, but rather resort to node and edge lists in the .json format Relatedness: The networks are the ego-networks for two related public accounts that are associated with citizen science (@EuCitSci & @SciStarter). Content: In this Zenodo entry, two files can be found. edges.json Represents the edge list, with the columns: source target wherefrom account EuCitSci 23929 friendslist EuCitSci Source and target are the necessary columns for the network creation and in this example indicate that EuCitSci follows 23929. wherefrom indicates the origin of this relation in the crawling (i.e., whether it was retrieved using the friends or follower list) and account indicates the ego-account it belongs to. Thus, the edges can also be separated using this account attribute as they represent two distinct networks. nodes.json Represents the nodes in the networks. The following data fields are contained: username CS followers_count friends_count ... 1116 CS 689 514 ... ... favourites_count listed_count statuses_count ... ... 2601 18 2141 ... ... degree in_degree out_degree ... ... 21 5 16 ... ... reciprocity account ... 0.48 EuCitSci Username represents the numerical and anonymised username, CS the community-membership. The different counts (e.g., followers_count) indicate the number of followers the user had at the time of the retrieval by the Twitter API. degree refers to the degree in the network (similarly the in- and out_degree), while reciprocity refers to the number of mutual edges in respect to the total number of edges per node (see here). Account is similar as described above. Grouping: The data is grouped according the ego-account it is associated to, as can be read above (i.e., the account attribute).

We collected the Radical Right On Twitter dataset (ROT7) to advance research into radical right activity online. The resource addresses a lack of data in this field, particularly data that relates to the activity of radical right actors. The dataset was funded without commercial support.

ROT follows six months of Twitter activity (8th July 2020 to 9th January 2021) from 35 radical right actors. We follow the advice given by Williams, Burnap, and Sloan (2017) for publishing Twitter data on sensitive topics. ROT includes:

It contains:

Actors' content: all content produced by the actors, including posts (n = 22,131), replies (n = 19,947), quotes (n = 11,314), and retweets (n = 37,283).

Actors' profiles: Twitter profile information for all 35 actors.

Actors' followers: a list of each actor's followers, collected each day (combined n = 6,592,056).

Actors' friends: a list of each actor's friends, collected each day (combined n = 262,856).

Direct engagement: all tweets which engage with actors, including replies, quotes and retweets (n = 31,443,828).

Engagers’ followers: List of followers of every user who replied, quoted or retweeted actors' content. We only collected users' list of followers once, even if they engaged with the actors multiple times during the period studied.

Other engagement: all other tweets collected through Twitter API that mentions an actor (n = 10,939,868).

Republican followers were coded as 0 and Democrat followers as 1.Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Logistic regression model with all predictors using data without outliers.

Analyze 9 months of Twitter ad performance data: $2.9k spent, 1,650 followers gained at $1.73 CPF. Key insights on social media ROI for startups.

This dataset contains all public posts collected from the social media platform X (formerly Twitter) that included the official hashtag #COP30noBrasil during the COP30 climate summit, between 10 and 21 November 2025. The data were retrieved using Tweet Binder and comprise a total of 1,139 interactions, including original posts, retweets and replies. For each entry, the dataset includes metadata describing user characteristics (such as verification status and follower–following ratio), content features (format, text, language), and interaction metrics (likes, reposts, replies). The dataset also includes derived analytical variables used in the associated research article. These include engagement, calculated as a percentage based on interaction metrics, and sentiment polarity, computed using the VADER (Valence Aware Dictionary and sEntiment Reasoner) Python library. The text fields were kept in their original languages, reflecting the multilingual nature of the conversation around COP30. This resource enables the examination of public discourse on climate negotiations, content diffusion dynamics, and the emotional tone of climate-related communication. It provides a structured and reusable dataset for researchers interested in climate communication, digital public spheres, social media analytics, and environmental politics.

Previous research has shown that political leanings correlate with various psychological factors. While surveys and experiments provide a rich source of information for political psychology, data from social networks can offer more naturalistic and robust material for analysis. This research investigates psychological differences between individuals of different political orientations on a social networking platform, Twitter. Based on previous findings, we hypothesized that the language used by liberals emphasizes their perception of uniqueness, contains more swear words, more anxiety-related words and more feeling-related words than conservatives’ language. Conversely, we predicted that the language of conservatives emphasizes group membership and contains more references to achievement and religion than liberals’ language. We analysed Twitter timelines of 5,373 followers of three Twitter accounts of the American Democratic and 5,386 followers of three accounts of the Republican parties’ Congressional Organizations. The results support most of the predictions and previous findings, confirming that Twitter behaviour offers valid insights to offline behaviour.

The number of X (Twitter) followers of the Major League Baseball team Miami Marlins increased substantially from September 2011 to November 2024. In the last recorded month, the team's social media account had around 0.42 million followers.

First column : User Second column: Friends that user have (random selected 2 people)

Republican followers were coded as 0 and Democrat followers as 1.Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Initial logistic regression model.

The relationships between journalists’ ideology and their Twitter friends’ and followers’ ideology.