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Title: Dark retweets : an investigation of non-conventional retweeting patterns
Author: Azman, Norhidayah
ISNI:       0000 0004 5347 1359
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2014
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Retweets are an important mechanism for the propagation of information on the Twitter social media platform. However, many retweets do not use the offcial retweet mechanism, or even community established conventions, and these "dark retweets" are not accounted for in many existing analyses. In this thesis, a typology of 19 different tweet propagation types is presented, based on seven characteristics: whether it is proprietary, the mechanism used, whether it is created by followers or non-followers, whether it mentions other users, if it is explicitly propagating another tweet, if it links to an original tweet, and the audience that it is pushed to. Based on this typology and two retweetability confidence factors, the degrees of a retweet's "darkness" can be determined. This typology was evaluated over two datasets: a random sample of 27,146 tweets, and a URL drill-down dataset of 262,517 tweets. It was found that dark retweets amounted to 20.8% of the random sample, however the behaviour of dark retweets is not uniform. The existence of supervisible and superdark URLs skew the average proportion of dark retweets in a dataset. Dark retweet behaviour was explored further by examining the average reach of retweet actions and identifying content domains in which dark retweets seem more prevalent. It was found that 1) the average reach of a dark retweet action (3,614 users per retweet) was found to be just over double the average reach of a visible retweet action (1,675 users per retweet), and 2) dark retweets were more frequently used in spreading social media (41% of retweets) and spam (40.6%) URLs, whilst they were least prevalent in basic information domains such as music (8.5%), photos (5%) and videos (3.9%). It was also found that once the supervisible and superdark URLs were discarded from the analysis, the proportion of dark retweets decreased from 20.8% to 12%, whilst visible retweets increased from 79.2% to 88%. This research contributes a 19-type tweet propagation typology and the findings that dark retweets exist, but their behaviour varies depending on the retweeter and URL content domain.
Supervisor: Millard, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HM Sociology ; QA76 Computer software