In the six months ending March 2024, the United States accounted for 23.21 percent of web traffic to the Twitter.com URL. Japan ranked second, accounting for 16.06 percent of traffic to the web page, and was followed by the United Kingdom, representing 5.51 percent of the web address online volume.

In March 2024, X's web page Twitter.com had *** billion website visits worldwide, up from *** billion site visits the previous month. Formerly known as Twitter, X is a microblogging and social networking service that allows most of its users to write short posts with a maximum of 280 characters.

Traffic analytics, rankings, and competitive metrics for twitter.com as of September 2025

In this letter, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000 km2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and U.K. census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71%-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R2 = 0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 h on the same day and for the same time in the following 2-3 days.

Between July and December 2024, over 335 million accounts on X (formerly Twitter) were suspended for reasons of spam or platform manipulation. User-informed labels were added to 66 million posts after being reported for spam.

1 Classification Dataset

This dataset for the classification model contains 3,804 tweets, where 1,902 are related to traffic accident reports (TA, positive class) and 1,902 are unrelated (NTA, negative class).

For training the tweet classification model, a collaborative labeling strategy was designed. Here, 30 people labeled data according to the instructions given. Each participant had to evaluate a tweet to manually classify it into one of three categories defined as: traffic accident related, unrelated and don´t know/no response. Each tweet was evaluated by 3 participants. The correct label was selected by voting; the 3 people must agree on the selected label, otherwise the tweet was excluded from training. This process took a month and required the development and deployment of a web application.

2 NER Dataset (Named Entity Recognition)

For the entity recognition model training, a sample of the filtered tweets resulting from the previous classification phase was taken. 1,340 tweets were extracted, where 800 are from “unofficial” users, almost 60% of the sample. These tweets were user reports on traffic incident occurred in Bogota from October 2018 to July 2019, including other tweets that contained some location references such as reports on the state of road infrastructure; some tweets from the years 2016 and 2017 were also included. Although these posts were not related to accidents per se, they were selected because they contained location information. The purpose was to train a model that would recognize these entities, because a classifier of accident-related tweets was previously created. Additionally, the dataset was split, reserving 1,072 tweets for training and 268 for evaluation.

This dataset was manually labeled using the IOB (Inside-outside- beginning) format. The labeling tool called Brat Annotation Tools was used for this task. The labels defined are Location, which refers to the location of the report; and Time, which refers to the time or date of the incident. Accordingly, 5 labels were generated: B-loc, I-loc, B-time, I-time and O. The O label refers to Others.

3 Traffic accident Twitter geolocation

A dataset with 26362 traffic accident tweets with the coordinates of the incident and the date of publication.

The US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

These are the key Twitter user statistics that you need to know.

Dataset

This dataset was created by Mounica B

Contents

Between July and December 2024, there were over four million account suspensions on X (formerly Twitter) and over 10.1 million posts either removed or labeled. Overall, 927,892 million account suspensions occurred for reasons of abuse or harassment, and 1.49 million posts were labeled or removed for this reason.

twitter.co is ranked #32862 in BR with 160.39K Traffic. Categories: . Learn more about website traffic, market share, and more!

twitter-thread.com is ranked #53126 in IN with 133.05K Traffic. Categories: . Learn more about website traffic, market share, and more!

As of October 2025, social network X (formerly known as Twitter) was most popular in the United States, with an audience reach of approximately 99.04 million users. Japan ranked second, recording more than 71 million users on the platform. Global Twitter usage As of the second quarter of 2021, X/Twitter had 206 million monetizable daily active users worldwide. The most-followed Twitter accounts include figures such as Elon Musk, Justin Bieber and former U.S. president Barack Obama. X/Twitter and politics X/Twitter has become an increasingly relevant tool in domestic and international politics. The platform has become a way to promote policies and interact with citizens and other officials, and most world leaders and foreign ministries have an official Twitter account. Former U.S. president Donald Trump used to be a prolific Twitter user before the platform permanently suspended his account in January 2021. During an August 2018 survey, 61 percent of respondents stated that Trump's use of Twitter as President of the United States was inappropriate.

This dataset includes a one percent sample of German-language Twitter retweets in Twitter raw data format. For each day, all retweets are stored in json data format (one entry per line).

The dataset was recorded using Tweepy and exported from a MongoDB database. It is intended to be imported into a MongoDB database to run analytical queries. It is not intended to be processed as is.

The dataset covers 60 consecutive days and ends on 01/25/2023.

The dataset was recorded as part of this study.

Kratzke, N. How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers. Computers 2023, 12, 57. https://doi.org/10.3390/computers12030057

Modeled probability of congestion for selected cities based on Twitter and OpenStreetMap data on a grid cell basis with a resolution of 100 meters. The data set includes the cities of Barcelona, ​​Berlin, Cincinnati, Kiev, London, Madrid, Nairobi, New York City, San Francisco, Sao Paulo and Seattle. The range of values ​​is from 0 (probably normal traffic flow) to 1 (high probability of traffic flow delay). Methodology: Based on Twitter and OpenStreetMap (OSM) data, a model was trained with the help of machine learning, which predicts the probability of traffic jams within the cities. Publicly provided data from UBER was used as reference data (https://movement.uber.com). The number of tweets and the number of points of interest from OSM near roads were used as indicators in the model. In addition, car journeys were simulated with the help of the openrouteservice based on the spatial distribution of the population and relevant POIs and taken into account in the model.

twitter-video-download.com is ranked #581767 in US with 29.02K Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

Advertising makes up 89% of its total revenue and data licensing makes up about 11%.

As of December 2022, X/Twitter's audience accounted for over *** million monthly active users worldwide. This figure was projected to ******** to approximately *** million by 2024, a ******* of around **** percent compared to 2022.

“10 days traffic” (T = 0.35, only BOTs’ posts, D = 30′′, S = 100 slots of equal size): MSE for different levels of smoothing.

Learn how to maximize Twitter content marketing ROI through data-driven recirculation. Analysis shows posts gain 75% of original retweets with each reshare.