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Dark retweets: an investigation of non-conventional retweeting patterns

Dark retweets: an investigation of non-conventional retweeting patterns
Dark retweets: an investigation of non-conventional retweeting patterns
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.
University of Southampton
Azman, Norhidayah
c4278d9d-e0e7-491c-a263-22064bee16ab
Azman, Norhidayah
c4278d9d-e0e7-491c-a263-22064bee16ab
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372

Azman, Norhidayah (2014) Dark retweets: an investigation of non-conventional retweeting patterns. University of Southampton, Physical Sciences and Engineering, Doctoral Thesis, 159pp.

Record type: Thesis (Doctoral)

Abstract

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.

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More information

Published date: June 2014
Organisations: University of Southampton, Web & Internet Science

Identifiers

Local EPrints ID: 368784
URI: https://eprints.soton.ac.uk/id/eprint/368784
PURE UUID: 02858166-556e-409c-a353-6e87e52e4d5d
ORCID for David Millard: ORCID iD orcid.org/0000-0002-7512-2710

Catalogue record

Date deposited: 03 Nov 2014 14:42
Last modified: 30 Nov 2018 01:36

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