3/1/2023 0 Comments Mega database 2019 torrent![]() ![]() The dataset has been continuously collected since January 22, 2020. The Twitter dataset contains tweets related to COVID-19. The dataset contains 7.6 million original tweets and 25.6 million retweets from 2.6 million unique users related to voter fraud claims. The dataset was accumulated to analyze rumors surrounding the 2020 election voter fraud. The dataset is available in Excel/CSV format and is segmented into 3 fields, namely tweet data, images, and videos. The dataset was amassed using Twitter’s Stream API over a period of 8 weeks (1 st Dec 2019 – ). Link- The Twitter Dataset comprises 60K random tweets from public Twitter accounts related to the search term “Covid-19”. Type- Corona Virus (Covid-19) Tweet Metadata Compilation 2020 I have also mentioned a method to get specific historical Twitter data of any kind, but before that, let’s discuss all the reliable sources to download free Twitter datasets.Ĭheck out all these sources to get free Twitter datasets for research purposes or analysis: I have compiled archives of various free Twitter datasets accumulated from various sources which can be very effective for someone looking for a reliable source of Twitter data sets. Twitter data sets can be effectively utilized in the areas of academic research, social projects, and studying marketing methodologies. Fill the form with your email address, search term, select the date ranges and submit.Īfter a while, you’ll receive your Twitter dataset and details associated with it.It’s really easy to download or get access to Twitter datasets of any search term or criteria, just follow these simple steps : In this post, we are going to discuss the reliable sources to acquire free Twitter datasets.ĭownload any required Twitter dataset using Trackmyhashtag: Twitter has set itself apart as a direct communication medium to a very large audience. Tweets are precise and convey the message in a snappy manner and this has led to Twitter becoming so popular that it is now influencing global landscapes. ![]()
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