Existing literature on retweets seems to focus mainly on retweets created using explicit, formal retweeting mechanisms, such as Twitter's own native retweet function, and the prefixing of the terms 'RT' or 'via' in front of copied tweets. However, retweets can also be made using implicit, informal mechanisms. These include tweet replies and other mechanisms, which use neither the native nor RT/via mechanisms, but their content and timelines suggest the likelihood of being a retweet. Moreover, retweets can also occur with or without a defined follower/following network path between a tweet originator and a retweeter. This paper presents an initial taxonomy of propagation based on seven different ways a tweet may spread: native, native non-follower, RT/Via, RT/Via non-follower, replies, non-follower replies and other implicit 'retweets'. An experiment has examined this new model, by investigating where tweets containing URLs from the domains of online petitions, charity fundraisers, news portals, and YouTube videos can be classified into the seven different categories. When including other implicit 'retweets', more than 50% of all the retweets found across all four domains were classified as implicit retweets, while more than 79% of all retweets were made by non-followers. More work needs to be done on the composition of other implicit 'retweets'. Initial investigations found hashtags in 99-100% of these tweets, suggesting that retweeting using conventional mechanisms may not be the main method that URLs get propagated across microblogs

Azman, Norhidayah

Millard, David

Weal, Mark

English

Southampton (e-Prints Soton)

Patterns of Implicit and Non-follower Retweet Propagation:Investigating the Role of Applications and HashtagsNorhidayah AzmanUniversity of SouthamptonSouthamptonUnited Kingdomnba08r@ecs.soton.ac.ukDavid MillardUniversity of SouthamptonSouthamptonUnited Kingdomdem@ecs.soton.ac.ukMark WealUniversity of SouthamptonSouthamptonUnited Kingdommjw@ecs.soton.ac.ukABSTRACTExisting literature on retweets seems to focus mainly onretweets created using explicit, formal retweeting mecha-nisms, such as Twitter's own native retweet function, andthe prexing of the terms `RT' or `via' in front of copiedtweets. However, retweets can also be made using implicit,informal mechanisms. These include tweet replies and othermechanisms, which use neither the native nor RT/via mech-anisms, but their content and timelines suggest the likeli-hood of being a retweet. Moreover, retweets can also oc-cur with or without a dened follower/following networkpath between a tweet originator and a retweeter. This pa-per presents an initial taxonomy of propagation based onseven dierent ways a tweet may spread: native, nativenon-follower, RT/Via, RT/Via non-follower, replies, non-follower replies and other implicit `retweets'. An experi-ment has examined this new model, by investigating wheretweets containing URLs from the domains of online peti-tions, charity fundraisers, news portals, and YouTube videoscan be classied into the seven dierent categories. Whenincluding other implicit `retweets', more than 50% of all theretweets found across all four domains were classied as im-plicit retweets, while more than 79% of all retweets weremade by non-followers. More work needs to be done onthe composition of other implicit `retweets'. Initial inves-tigations found hashtags in 99{100% of these tweets, sug-gesting that retweeting using conventional mechanisms maynot be the main method that URLs get propagated acrossmicroblogs.Categories and Subject DescriptorsH.3.1 [Information Systems]: Information Storage andRetrieval|Content Analysis and IndexingGeneral TermsAlgorithms, Measurements, Experimentation1. INTRODUCTIONRetweets have been acknowledged for their conversationalnature [1]. Several studies have been made on the patternsof retweet propagation, particularly in the areas of retweetreach [5], information brokerage [7] and in approximatinginuence [4, 2].However, these studies have been focused on retweets madeusing explicit retweeting mechanisms. These include twoconventional retweeting mechanisms; the native retweet func-tion provided by Twitter (native retweet), and also by copy-ing and pasting prior tweets and then inserting terms suchas `RT' or `via' to signify a retweet (RT/via retweet).There seems to be little emphasis on retweets made usingimplicit mechanisms. Examples of implicit retweets includethose that were spread using the reply function, rather thanthe retweet function. Implicit retweets could also includethose that show no discernable markers signifying a retweet,but the content could have been seen beforehand prior to be-ing subsequently repeated. In these instances, it is dicultto determine the provenance of retweets made implicitly [1].Another area of interest in this eld is the proportion ofretweets made by users who do not follow the authors oforiginating tweets [6]. It was found that 33% of retweetsgave credit to users who were not being followed [3].In this paper, we studied the dierent ways that a tweet mayspread, forming a taxonomy of propagation types. The nd-ings in this paper show that implicit retweets form a largeproportion of all types of retweets. This suggests that it maybe important to study implicit retweets in more depth whenanalyzing information propagation in social media. Thisresearch also studied the role of hashtags and the Twitterapplications involved in implicit retweeting.Section 2 explains the taxonomy of propagation types basedon seven dierent ways a tweet could spread. Section 3describes the experiment that was run to validate this tax-onomy. The subsequent Section 4 details all the ndings,followed by the conclusion in Section 5.2. TAXONOMYOFPROPAGATIONTYPESThe aim of this study is to explore the existence of implicitand non-follower retweets, and comparing them to explicitand follower retweets. These patterns describe the dierentmechanisms that could be used to propagate an item acrossan interlinked environment such as microblogs.From the two aspects described above, a taxonomy of sevendierent ways a tweet could spread was constructed (see Fig-ure 1): native retweet, native non-follower, RT/via retweet,RT/via non-follower, replies, non-follower replies, and otherimplicit retweets.Table 1 shows the classications of explicit, implicit, followerand non-follower retweet types.Figure 1: Retweet decision model based on taxon-omy of propagation typesExplicit RTs Implicit RTsFollower RTs N @ RVNon-follower RTs NnF @nFRVnF OTable 1: Matrix of explicit/implicit andfollower/non-follower retweet types2.1 Native Retweets (N)Native retweets are dened as tweets which use the retweetmechanism provided either by Twitter's proprietary user in-terfaces (webpage, apps) or by the Twitter API. Third partyapps which send retweets via Twitter API's retweet functionare also included in this count.2.2 Native Non-follower Retweets (NnF)Native non-follower retweets are native retweets which in-clude non-follower paths. For example, user B made a na-tive retweet to his or her followers, and the tweet text wasreferencing user A. However, user B is not following user A,thus this connection between users B and A is categorizedas a native non-follower retweet.2.3 RT/Via Retweets (RV)RT/Via retweets are dened as tweets which repeat priortweets and include any of the following tokens within thetweets: \rt @", \rt@", \rt:@", \rt: @" , \retweet @", \via@", \retweet :@", \r/t", \rt:", \RT @", \RT@", \RT:@", \RT:@", \RETWEET @", \VIA @", \RETWEET :@", \R/T", or\RT:".These tweets include any tweets made by third-party appswhich cut and paste prior tweets and prexes any of theabove tokens onto the tweets before posting them via theTwitter API. This is dierent to using Twitter API's ownproprietary retweet function.2.4 RT/Via Non-follower Retweets (RVnF)RT/Via non-follower retweets are dened as the non-followerpath that is observed amongst RT/Via retweets. For exam-ple, user E made an RT/Via retweet of a text which ref-erenced user D. However, user E is not following user D.Therefore, the connection between the two users is catego-rized as an RT/Via non-follower retweet.2.5 Replies (R)Replies are dened as tweets which begin with a mentionto another user. This research work focuses specically ontweet replies where one user sends a URL to another userdirectly.The dierence between a tweet reply and a direct message isthat replies can be viewed publicly by followers of both usersinvolved and also the general public browsing through eitheruser's Twitter prole page. This is unlike direct Twittermessages that can only be seen by the two users involved inthe correspondence.The main reason why replies are considered interesting, andtherefore included in this taxonomy, is that this research'sexperimental toolkit found several instances where URLswere being propagated via replies. A subset of these repliesincluded URLs which had been seen before. For example,User A sees a URL propagated from another user, and thenUser A sends that same URL to followers via replies.This propagation type may not traditionally be consideredas a retweet type, but considering that this toolkit onlyrecords replies which contain URLs that had already beenseen before by the user making the reply, thus this replybecomes a valid point of reference in determining the prove-nance and propagation of URLs across tweets.This research work acknowledges that the visibility of repliescould be dicult to determine accurately. If user A tweets`hello!', then all of A's followers would see that tweet appearin their subscribed timelines of tweets.However, when a reply is made, then that tweet would onlyappear in the timelines of mutual followers of all the partiesinvolved in that particular correspondence. For example,if users A and B were replying tweets amongst themselves,then only mutual followers of both A and B would see thosereplies in their subscribed timelines.Nonetheless, if someone were to browse the Twitter prolepages of either A and B, then anyone could see those repliespublicly.Given the complexities of tweet visibility pertaining to replies,it can be slightly harder to correctly gauge how many peoplecould potentially have seen a reply displayed on Twitter. Al-though a reply is publicly visible to non-followers, it is morelikely to be seen by mutual followers of the parties involvedin the reply, as opposed to the general public.2.6 Non-follower Replies (@nF)Non-follower replies similarly begin with a user mention. Asan example, user J sent a tweet containing a URL to userK. Then, user K made a reply to user L. In this case, userK is not following user J, thus this connection is categorizedas a non-follower reply.2.7 Other Implicit ‘Retweets’ (OnF)Other implicit `retweets' are dened as tweets which do notconform to any of the above six classications described.For example, user Y is not following user X, but makes aretweet without using an explicit mechanism; the retweetwas not made using native, RT/via or reply mechanisms.These also include tweets which seem to have no apparentattribution or acknowledgement of prior tweets or origina-tors from any time before the retweet was made. For exam-ple, tweets being copied verbatim would be categorized asan other implicit `retweet'.Some assumptions had to be made when looking at otherimplicit `retweets'. Some of these could be original tweetswhich were created without referencing any prior tweets.Moreover, a user could click on a `share this' button ona third-party website, which then shares a particular URLlink to that user's Twitter followers without using any ex-plicit retweet mechanisms. Therefore, this research acknowl-edges that some of the tweets considered as an other implicit`retweet' may include original tweets, or come from third-party sources such as non-Twitter web pages, user e-mailsand so on.This research holds the assumption that Twitter users ref-erence these URLs after seeing prior tweets which promptthem to visit these websites. This assumption seems intu-itive in the case of online petitions, because user A could signa petition and then tweet the petition's URL link to followeruser B. Then, it is possible for user B to also sign the peti-tion and subsequently post the same URL link to user B'sown followers, without acknowledging user A's prior tweet.3. EXPERIMENT SETUPAn experiment was run to validate this taxonomy against acollection of tweets containing URLs from four chosen do-mains, namely online petitions, charity fundraisers, newsportals, and YouTube videos. These domains were chosenafter manually observing the types of URLs that seem to bebe propagating across Twitter.The experimental toolkit built for this research work consistsof a suite of Python scripts. The toolkit is exploratory innature and used mainly to investigate the types of retweetsfound o of the Twitter API.Five dierent URLs were chosen for each domain, giving atotal of 20 URLs overall. The experimental toolkit wasused to collect tweets containing those URLs, and alsorecord the follower/following networks of all the Twit-ter users involved within the collected dataset of tweets. Atotal of 11,846 tweets were collected, involving 7118 uniqueusers.4. FINDINGSAcross all four domains, the proportions of implicit retweetsseem to be consistently bigger than explicit retweets. More-over, non-follower retweets also form bigger proportions ascompared to follower retweets (see Table 2).Domain Retweets (%)Exp Imp F nFFundraisers 25.9 74.1 13.6 86.4News 2.4 97.6 1.5 98.5Petitions 48.5 51.5 21.3 78.7YouTube 23.1 76.9 12.1 87.9Table 2: Proportions of explicit/implicit andfollower/non-follower retweets across all four do-mainsAcross all four domains, two observations seem to consis-tently emerge: 1. There are more implicit retweets as op-posed to explicit retweets (proportions of implicit retweetsrange from 51.5% to 97.6%), and 2. There are more non-follower retweets as opposed to follower retweets (propor-tions of non-follower retweets range from 78.7% to 98.5%)In the news domain, we can see an extremely high propor-tion of implicit retweets (97.6%), while tweets containingpetition URLs seem to have a somewhat 50/50 spread of ex-plicit and implicit retweet types. This may suggest that thetype of URL being propagated could be a determining fac-tor in how subsequent retweets are made, particularly whichmechanism would be used.The proportions found by this toolkit also support the ideathat there exists alternative propagation pathways that couldbe useful in quantifying explicit and implicit power.Given this preliminary nding, further analysis was car-ried out to investigate the composition of other implicit`retweets', particularly at hashtags and Twitter applications.For each domain, the proportions of retweets containinghashtags were recorded, and the types of Twitter applica-tions that were used were identied.In this analysis, hashtags were found between 98{100% ofother implicit `retweets'. This suggests that users could haveseen particular URLs from people they do not follow viahashtags before subsequently retweeting them (see Table 3).The top three Twitter applications found for each domainis listed in Table 4. There seems to be no pattern thatcould be identied from these rankings. Interestingly, in thedomain of YouTube URLs, the top three applications are allproprietary interfaces created by Twitter.Domain With # (%) Without # (%)Fundraisers 98.6 1.4News 98.8 3.2Petitions 100.0 0.0YouTube 100.0 0.0Table 3: Proportions of implicit retweets containinghashtagsD 1st 2nd 3rdF TweetDeck Tweet Button FoursquareN dlvr.it DestroyTwitter TweetMemeP HootSuite CoTweet Twitter foriPhoneY twitter.com Twitter for Twitter foriPhone AndroidTable 4: Rankings of Twitter applications makingimplicit retweets4.1 Implicit and Non-follower Retweets as aMedium for PropagationAs mentioned above, throughout all four domains, the pro-portion of implicit retweets was consistently bigger than anyother retweet type observed by this toolkit.This suggests that when looking at the propagation of retweets,looking only at explicit retweets such as native and RT/Viaretweets may not oer a complete picture of a full propaga-tion pattern. Based on the above ndings, more than halfof the URLs were tweeted without using the above retweetmechanisms. Therefore, more work needs to be done toidentify how these `dark' retweets propagate.When the `dark' retweets are analyzed manually, the tweetsseem to consist of either one of the following characteristics:verbatim copies of other tweets, unknown retweet markers,or non-Latin characters.Prior work on retweets seems to be concentrated on study-ing retweets which conform to explicit, pre-dened retweetmechanisms. There seems to be little work done on the roleof replies, and implicit or non-follower retweets, particularlywhere Twitter users get acknowledged in retweets made byother people who are not their followers.From the ndings in this experiment, the prevalence of im-plicit retweets seems to suggest that a large proportion ofpeople do no follow normal retweet mechanisms. This obser-vation raises questions as to how tweets are normally per-ceived to be propagated; tweets seem to spread not justvia retweets, but they could also spread via implicit meanssuch as verbatim copying or using non-conventional retweettokens in their tweets. All this suggests the possibility ofa dierent way for tweets to propagate across the Twitternetwork.5. CONCLUSIONThis paper has contributed a taxonomy of propagation typesbased on seven dierent method of spreading a tweet. Thistaxonomy provides a novel way of viewing retweeting be-haviour and it may lead to a re-evaluation of some existingresearch.In the experiment to validate this taxonomy, more than 50%of all retweets were classied as implicit retweets across allfour domains of URLs experimented on. In addition, morethan 79% of all retweets were made by non-followers. Hash-tags usage seems to be a plausible explanation to this highpercentage. On all four domains, more than 99{100% ofother implicit `retweets' contained hashtags in them.This initial taxonomy is currently being rened, particularlyto separate original tweets from implied retweets within theother implicit `retweet' category.At present, this research has shown that implicit retweet-ing plays a signicant role in the propagation of informationthrough the social media space and must be considered whenlooking at issues such as inuence within social microblog-ging.6. REFERENCES[1] d. boyd, S. Golder, and G. Lotan. Tweet, tweet,retweet: Conversational aspects of retweeting onTwitter. In Hawaii International Conference on SystemSciences, pages 1{10, Los Alamitos, CA, USA, 2010.IEEE Computer Society.[2] M. Cha, H. Haddadi, F. Benevenuto, and K. P.Gunmadi. Measuring user inuence in Twitter: Themillion follower fallacy. In International AAAIConference on Weblogs and Social Media FourthInternational AAAI Conference on Weblogs and SocialMedia. AAAI, 2010.[3] W. Galuba, K. Aberer, D. Chakraborty, Z. Despotovic,and W. Kellerer. Outtweeting the twitterers- predictinginformation cascades in microblogs. In 3rd Workshopon Online Social Networks, Boston, MA, USA, 2010.USENIX.[4] H. Kong, H. Park, and S. Han. The talkative, thepopular, and the inuential Korean Twitter users. InSocial Networks Interoperability 1st InternationalWorkshop 2009, 2009.[5] H. Kwak, C. Lee, H. Park, and S. Moon. What isTwitter, a social network or a news media? InProceedings of the 19th international conference onWorld wide web, pages 591{600, Raleigh, NorthCarolina, USA, 2010. ACM.[6] M. Nagarajan, H. Purohit, and A. Sheth. A qualitativeexamination of topical tweet and retweet practices. InInternational AAAI Conference on Weblogs and SocialMedia Fourth International AAAI Conference onWeblogs and Social Media. AAAI, 2010.[7] D. van Liere. How far does a tweet travel?: Informationbrokers in the twitterverse. In Proceedings of theInternational Workshop on Modeling Social Media,pages 1{4, Toronto, Ontario, Canada, 2010. ACM.

A qualitative examination of topical tweet and retweet practices.

How far does a tweet travel?: Information brokers in the twitterverse.

Measuring user in uence in Twitter: The million follower fallacy.

Outtweeting the twitterers- predicting information cascades in microblogs.

retweet: Conversational aspects of retweeting on Twitter.

The talkative, the popular, and the in uential Korean Twitter users.

What is Twitter, a social network or a news media?

Patterns of implicit and non-follower retweet propagation: investigating the role of applications and hashtags

Patterns of implicit and non-follower retweet propagation: investigating the role of applications and hashtags

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