2026-09-18T21:42:53Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/973482022-01-13T07:54:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Roy E. Welsch.
Sakamoto, Tomoaki
Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Massachusetts Institute of Technology. Computation for Design and Optimization Program
2015-06-10T19:12:21Z
2015-06-10T19:12:21Z
2015
2015
http://hdl.handle.net/1721.1/97348
910560906
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-82).
This dissertation examines retweeting activities as the information spreading function of Twitter. First, we investigated what kind of features of a tweet help to get retweets. We construct a model that describes peoples' decision making on retweets, and with related observation, we show that more retweeted tweets get retweeted more. In terms of specific features of tweets, it has been shown that the number of followers and the number of retweets are positively correlated, and hashtags attract more retweets than the tweets without hashtags. On the other hand, we also found that including hashtags and getting one or more retweets are statistically independent. Moreover, we showed including URLs or user-mentions in tweets and getting one or more retweets are statistically independent. In our results, including a picture is slightly effective to get this sense of retweetability. Second, we compare the retweeters of tweets including a picture and only text, especially focusing on distance from the original tweeters. Comparing the ratio of retweets by followers of the author of the original tweets among the initial 50 retweets, tweets with a picture have a slightly lower ratio, though there is no significant difference between the average for tweets with pictures and without pictures at the 95% significance level. We also investigate how many retweets are posted by users in followers' network connected to the original tweeter, and show that the depths of retweeters' network for tweets with picture have larger variance than that of tweets without pictures. This result implies that a tweet including picture can reach more people than a tweet without a picture potentially.
by Tomoaki Sakamoto.
S.M.
82 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Computation for Design and Optimization Program.
An analysis on information diffusion by retweets in Twitter
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>This dissertation examines retweeting activities as the information spreading function of Twitter. First, we investigated what kind of features of a tweet help to get retweets. We construct a model that describes peoples' decision making on retweets, and with related observation, we show that more retweeted tweets get retweeted more. In terms of specific features of tweets, it has been shown that the number of followers and the number of retweets are positively correlated, and hashtags attract more retweets than the tweets without hashtags. On the other hand, we also found that including hashtags and getting one or more retweets are statistically independent. Moreover, we showed including URLs or user-mentions in tweets and getting one or more retweets are statistically independent. In our results, including a picture is slightly effective to get this sense of retweetability. Second, we compare the retweeters of tweets including a picture and only text, especially focusing on distance from the original tweeters. Comparing the ratio of retweets by followers of the author of the original tweets among the initial 50 retweets, tweets with a picture have a slightly lower ratio, though there is no significant difference between the average for tweets with pictures and without pictures at the 95% significance level. We also investigate how many retweets are posted by users in followers' network connected to the original tweeter, and show that the depths of retweeters' network for tweets with picture have larger variance than that of tweets without pictures. This result implies that a tweet including picture can reach more people than a tweet without a picture potentially.</Abstract>
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