SNAP (Stanford Network Analysis Platform) Large Network Dataset Collection,
Jure Leskovec and Anrej Krevl, http://snap.stanford.edu/data, June 2014.
email: jure at cs.stanford.edu
476 million Twitter tweets
Dataset information
467 million Twitter posts from 20 million users covering a 7 month period
from June 1 2009 to December 31 2009. We estimate this is about 20-30% of
all public tweets published on Twitter during the particular time frame.
For each public tweet the following information was available:
Author
Time
Content
We have no Twitter social graph (who-follows-whom graph) available. You can
find a copy of the graph at http://an.kaist.ac.kr/traces/WWW2010.html
(thanks to Haewoon Kwak, et al.).
Dataset statistics
Number of users 17,069,982
Number of tweets 476,553,560
Number of URLs 181,611,080
Number of Hashtags 49,293,684
Number of re-tweets 71,835,017
Source (citation)
J. Yang, J. Leskovec. Temporal Variation in Online Media. ACM Intl.
Conf. on Web Search and Data Mining (WSDM '11), 2011.
As per request from Twitter the data is no longer available.
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http://an.kaist.ac.kr/traces/WWW2010.html :
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What is Twitter, a Social Network or a News Media?
Haewoon Kwak (http://an.kaist.ac.kr/~haewoon),
Changhyun Lee (http://an.kaist.ac.kr/~chlee),
Hosung Park (http://an.kaist.ac.kr/~hosung),
and Sue Moon (http://an.kaist.ac.kr/~sbmoon)
Proceedings of the 19th International World Wide Web (WWW) Conference,
April 26-30, 2010, Raleigh NC (USA)
Twitter, a microblogging service less than three years old, commands more
than 41 million users as of July 2009 and is growing fast. Twitter users
tweet about any topic within the 140-character limit and follow others to
receive their tweets. The goal of this paper is to study the topological
characteristics of Twitter and its power as a new medium of information
sharing.
We have crawled the entire Twitter site and obtained 41.7 million user
profiles, 1.47 billion social relations, 4,262 trending topics, and 106
million tweets. In its follower-following topology analysis we have found a
non-power-law follower distribution, a short effective diameter, and low
reciprocity, which all mark a deviation from known characteristics of human
social networks~\cite{Newman03}. In order to identify influentials on
Twitter, we have ranked users by the number of followers and by PageRank
and found two rankings to be similar. Ranking by retweets differs from the
previous two rankings, indicating a gap in influence inferred from the
number of followers and that from the popularity of one's tweets. We have
analyzed the tweets of top trending topics and reported on their temporal
behavior and user participation. We have classified the trending topics
based on the active period and the tweets and show that the majority (over
85%) of topics are headline news or persistent news in nature. A closer
look at retweets reveals that any retweeted tweet is to reach an average of
1,000 users no matter what the number of followers is of the original
tweet. Once retweeted, a tweet gets retweeted almost instantly on next
hops, signifying fast diffusion of information after the 1st retweet.
To the best of our knowledge this work is the first quantitative study on
the entire Twittersphere and information diffusion on it.
http://an.kaist.ac.kr/~haewoon/papers/2010-www-twitter.pdf
@inproceedings{Kwak10www,
author = {Kwak, Haewoon and Lee, Changhyun and Park, Hosung and
Moon, Sue},
title = "{W}hat is {T}witter, a social network or a news media?",
booktitle = {WWW '10: Proc. the 19th Intl. Conf. on World Wide Web},
year = {2010},
isbn = {978-1-60558-799-8},
pages = {591--600},
location = {Raleigh, North Carolina, USA},
doi = {http://doi.acm.org/10.1145/1772690.1772751},
publisher = {ACM},
address = {New York, NY, USA},
}
Slides:
https://www.slideshare.net/haewoon/
what-is-twitter-a-social-network-or-a-news-media-3922095
Due to Twitter's new Terms of Services, we cannot share data containing
tweets any more. (for more info, read RWW's article "How Recent Changes to
Twitter's Terms of Service Might Hurt Academic Research")
Social graph
Format
USER \t FOLLOWER \n
* USER and FOLLOWER are represented by numeric ID (integer).
* These numeric IDs are the same as numeric IDs Twitter managed.
* For details, see Twitter API Page
Example
12 13
12 14
12 15
16 17
* Users 13, 14 and 15 are followers of user 12.
* User 17 is a follower of user 16.
Restricted user profiles (> 10,000 followers):
celebrities_profiles.txt
Format
numeric_id \t verified \t profile_sidebar_fill_color \t profile_text_color
\t followers_count \t protected \t location \t profile_background_color \t
utc_offset \t statuses_count \t description \t friends_count \t
profile_link_color \t profile_image_url \t notifications \t
profile_background_image_url \t screen_name \t profile_background_tile \t
favourites_count \t name \t url \t created_at \t time_zone \t
profile_sidebar_border_color \t following \t gender (infered by name) \n
* All fields except gender are returned by user method of Twitter API
* For the description of each field see Returns Values page in Twitter
API Wiki
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Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:
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The SNAP data set is 1-based. In the SuiteSparse Matrix collection, the
celebrity profiles are stored in Problem.aux.*, using the same names as
above, except for the first ("numeric_id" is replaced with "celebrity_id").
The gender is excluded from the SuiteSparse Matrix Collection since it is
only inferred from first names, and not part of the original Twitter data.
Inferring gender from first names is very error prone.
The userid's range from 12 to 61,578,414, but of those userid's, 19,926,184
have no followers and no one follows them. The graph Problem.A only holds
the Twitter follower graph for non-empty nodes. A(i,j)=1 if user with
nodeid(i) is followed by user with nodeid(j). For example, the first two
non-empty userid's are nodeid(1)=12 and nodeid(2)=13, and the first edge is
(12,13). Thus A(1,2)=1 since nodeid(1)=12 is followed by nodeid(2)=13.
The user id's of the nodes in the graph are held in Problem.aux.nodeid.
