User Tools

Site Tools


multi-hashtags

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

multi-hashtags [11-Apr-14 11:55]
sanjay created
multi-hashtags [11-Apr-14 12:27] (current)
sanjay
Line 6: Line 6:
  
 ====Analytic Work==== ====Analytic Work====
-We used [[Software Tools|Chorus]] to plot a '​cluster map' of key multi-hashtags (below). The Chorus software suite includes a Culture Explorer feature, which enables the co-occurrence of any terms within a Tweet to be identified. ​+We used [[Software Tools|Chorus]] to plot a '​cluster map' of key multi-hashtags (fig. 1). The Chorus software suite includes a Culture Explorer feature, which enables the co-occurrence of any terms within a Tweet to be identified. ​
  
-{{ ::​notracist-cluster-explorer.jpg |}}+<​imgcaption image1|Hashtag cluster map>{{ ::​notracist-cluster-explorer.jpg |}}</​imgcaption>​
  
 In this map, each node (i.e. point in the map) is a hashtag, and the position and connectivity of nodes is determined by how frequently those hashtags are used together (co-occurrence),​ such that '​similar'​ hashtags cluster together (i.e. ones which are used in comparable ways to express similar sentiments). The model reveals how hashtags relate to each other as ‘semantic’ entities. The two radials overlaid on the image facilitate the process of reading the visualisation. In this map, each node (i.e. point in the map) is a hashtag, and the position and connectivity of nodes is determined by how frequently those hashtags are used together (co-occurrence),​ such that '​similar'​ hashtags cluster together (i.e. ones which are used in comparable ways to express similar sentiments). The model reveals how hashtags relate to each other as ‘semantic’ entities. The two radials overlaid on the image facilitate the process of reading the visualisation.
Line 16: Line 16:
 \\ \\
 \\ \\
-There are also a number of significantly populated (i.e. larger in area) nodes that feature on the outer branches extending from this central cluster (including //#truth//, //#​iswear//,​ //#fact// and //#​justsayin/​g//,​ often appearing on the end of branches. These are demarcated in the outer (purple) radial.+There are also a number of significantly populated (i.e. larger in area) nodes that feature on the outer branches extending from this central cluster (including //#truth//, //#​iswear//,​ //#fact// and //#​justsayin/​g//,​ often appearing on the end of branches. These are demarcated in the outer (blue) radial.
 \\ \\
 \\ \\
 The difference between the two radials is significant in as much they illustrate different tweeting practices, and we find that the inner radial consists largely of '​Humour'​-type hashtags which are intended to mark tweet content as containing jokes or other comedic material; whilst the outer radial consists mostly of '​Truth'​-type hashtags which tweeters use to clarify or qualify their statements by referring to them as observations and facts. ​ The difference between the two radials is significant in as much they illustrate different tweeting practices, and we find that the inner radial consists largely of '​Humour'​-type hashtags which are intended to mark tweet content as containing jokes or other comedic material; whilst the outer radial consists mostly of '​Truth'​-type hashtags which tweeters use to clarify or qualify their statements by referring to them as observations and facts. ​
 \\ \\
-A further step in the analysis filtered the data into two sub-datasets representing '​Humour'​ and '​Truth'​ hashtags. To do this, we used the cluster map itself as a way of indicating which hashtags were associated with either category - broadly speaking, hashtags which featured in the inner radial were judged to be part of the '​Humour'​ category, and hashtags which featured in the outer radial towards the ends of topical branches were judged to be part of the '​Truth'​ category. ((The #​justsayin/​g hashtag ​can also belong to the Humour category, though guided by the visualization,​ we located it in the Truth category.))+\\ 
 +A further step in the analysis filtered the data into two sub-datasets representing '​Humour'​ and '​Truth'​ hashtags. To do this, we used the cluster map itself as a way of indicating which hashtags were associated with either category - broadly speaking, hashtags which featured in the inner radial were judged to be part of the '​Humour'​ category, and hashtags which featured in the outer radial towards the ends of topical branches were judged to be part of the '​Truth'​ category.((The #​justsayin/​g hashtag ​could also belong to the Humour category, though guided by the visualization,​ we located it in the Truth category.))
  
 ==='​Humour'​ and '​Truth'​ - Practices of Hashtagging=== ==='​Humour'​ and '​Truth'​ - Practices of Hashtagging===
-Having split our dataset in to the Humour and Truth categories, it was noticeable that both sub-datasets shared a basic lexicon – terms(words) such as '​black,​ '​white',​ '​people',​ '​like'​ and '​just'​ feature prominently in both '​Humour'​ and '​Truth'​ sub-datasets. It seems improbable that there is a linguistic or semantic means of consistently distinguishing between either category when attempting to interpret the meaning of a tweet. What is significant is that the //words// used in both '​Humour'​ and '​Truth'​ categories are broadly similar, and it is the //tweeting practices// associated with each category that accounts for their distinction. To demonstrate this, see the two images below which represent cluster maps plotting individual words contained within the '​Humour'​ and '​Truth'​ sub-datasets (respectively):+Having split our dataset in to the Humour and Truth categories, it was noticeable that both sub-datasets shared a basic lexicon – terms (words) such as '​black,​ '​white',​ '​people',​ '​like'​ and '​just'​ feature prominently in both '​Humour'​ and '​Truth'​ sub-datasets. It seems improbable that there is a linguistic or semantic means of consistently distinguishing between either category when attempting to interpret the meaning of a tweet. What is significant is that the //words// used in both '​Humour'​ and '​Truth'​ categories are broadly similar, and it is the //tweeting practices// associated with each category that accounts for their distinction. To demonstrate this, see the two images below which represent cluster maps plotting individual words contained within the '​Humour' ​(fig.2 ) and '​Truth'​ sub-datasets (fig. 3).
 \\ \\
-{{ :​humour-cluster.jpg |IMAGE (UPLOAD VIA MEDIA MANAGER)}} 
 \\ \\
-{{ :​truth-cluster.jpg |}}+<​imgcaption image2|'​Humour'​ cluster map>{{ :​humour-cluster.jpg |}}</​imgcaption>​ 
 +\\ 
 +<​imgcaption image3|'​Truth'​ cluster map>{{ :​truth-cluster.jpg |}}</​imgcaption>​
 \\ \\
 These cluster maps are visually different from each other - the '​Humour'​ map is a messy set of interconnected branching terms, whereas the '​Truth'​ map shows a high degree of term density around the far edges of the map (i.e. as far away from each other as the cluster map model will allow). The explanation for the different visualisations lies in the tweeting practices associated with each category. Typically, we find: These cluster maps are visually different from each other - the '​Humour'​ map is a messy set of interconnected branching terms, whereas the '​Truth'​ map shows a high degree of term density around the far edges of the map (i.e. as far away from each other as the cluster map model will allow). The explanation for the different visualisations lies in the tweeting practices associated with each category. Typically, we find:
Line 37: Line 39:
   * //​KokoBugz//:​ RT @AlanCaravaggio:​ How white people react to black athletes #funny #revine #loop #notracist #VineStar https://​t.co/​********   * //​KokoBugz//:​ RT @AlanCaravaggio:​ How white people react to black athletes #funny #revine #loop #notracist #VineStar https://​t.co/​********
   * //​KoryBoolet//:​ #​whitepeopleproblems #howto #remake #NotRacist #comedy #funny #cute #magic #loop #unPOP #see #​drivingvine https://​t.co/​**********   * //​KoryBoolet//:​ #​whitepeopleproblems #howto #remake #NotRacist #comedy #funny #cute #magic #loop #unPOP #see #​drivingvine https://​t.co/​**********
-\\ + 
-In the '​Humour'​ category, multi-hashtags are frequently used together - this accounts for their tight central clustering visible in our original hashtag cluster map. These hashtags are drawn from a relatively narrow pool of terms which are repeatedly used. '​Humour'​ as a type of racialized talk relies on an implicitly-agreed-upon set of general classificatory hashtags which users recognise and drawn on in order to situate their tweets as embodying racialised humour (rather than actual racist intent). The '​Humour'​ hashtags themselves do not explain the meaning of the tweet, because the hashtags themselves - as a kind of self-referential meta-data(({{http://​www.academia.edu/​attachments/​30315675/​download_file| Ross, S., nd. Hashtags, Algorithmic Compression,​ and Henry James’s Late Style (Draft)}})) - //are// the tweet. Humour-type tweeting appear to proliferate a repetitious cacophony of racialized meanings and affects. ​+In the '​Humour'​ category, multi-hashtags are frequently used together - this accounts for their tight central clustering visible in our original hashtag cluster map (fig. 1). These hashtags are drawn from a relatively narrow pool of terms which are repeatedly used. '​Humour'​ as a type of racialized talk relies on an implicitly-agreed-upon set of general classificatory hashtags which users recognise and drawn on in order to situate their tweets as embodying racialised humour (rather than actual racist intent). The '​Humour'​ hashtags themselves do not explain the meaning of the tweet, because the hashtags themselves - as a kind of self-referential meta-data(({{http://​www.academia.edu/​attachments/​30315675/​download_file| Ross, S., nd. Hashtags, Algorithmic Compression,​ and Henry James’s Late Style (Draft)}})) - //are// the tweet. Humour-type tweeting appear to proliferate a repetitious cacophony of racialized meanings and affects. ​
 ===(ii) '​Truth'​ Multi-Hashtags=== ===(ii) '​Truth'​ Multi-Hashtags===
 '​Truth'​ hashtags are, in contrast to their '​Humour'​ counterparts,​ used more sparingly and are drawn from a much wider pool of hashtag terms. Where the usage of '​Humour'​ multi-hashtags conforms to the well-understood function of hashtags as //rendering something searchable//,​ '​Truth'​ multi-hashtags are deployed differently as a means of clarifying or qualifying the semantic content of tweets. For example: '​Truth'​ hashtags are, in contrast to their '​Humour'​ counterparts,​ used more sparingly and are drawn from a much wider pool of hashtag terms. Where the usage of '​Humour'​ multi-hashtags conforms to the well-understood function of hashtags as //rendering something searchable//,​ '​Truth'​ multi-hashtags are deployed differently as a means of clarifying or qualifying the semantic content of tweets. For example:
Line 44: Line 46:
   * //​J3N5TT3R//:​ Asian guys only have two volumes, quiet and shout. The ones on the next table are stuck on shout #notracist #fact   * //​J3N5TT3R//:​ Asian guys only have two volumes, quiet and shout. The ones on the next table are stuck on shout #notracist #fact
   * //​christophe1435//:​ This economics tutorial is like 95% Asian. #notracist #truth   * //​christophe1435//:​ This economics tutorial is like 95% Asian. #notracist #truth
-\\ + 
-Here, the usage of multi-hashtags indicates how the tweeter intends for their tweet to be interpreted - their '​stance'​ ((Zapavigna,​ M., 2011. Ambient Affiliation:​ A Linguistic Perspective on Twitter. New Media & Society, 13(5), pp. 788–806.)) - as not representing any racist intent (i.e. //#​notracist//​) and //​justifying//​ this disaffiliation with racism because the tweeter is stating what they argue is a defensible or observable everyday truth (e.g. //#​justsayin//​). This gives the '​Truth'​ cluster map above its distinctive outer density pattern - the wide variety of largely non-associated terms appear as distantly related from other, due to their lack of co-occurrence together. ‘Truth’ as a type of racialized talk relies on a diverse array of largely single-use hashtags which users draw on not to participate in wider debate or discussion, but to make explicit their semantic intentions (however misplaced or ignorant).+Here, the usage of multi-hashtags indicates how the tweeter intends for their tweet to be interpreted - their '​stance'​ ((Zapavigna,​ M., 2011. Ambient Affiliation:​ A Linguistic Perspective on Twitter. New Media & Society, 13(5), pp. 788–806.)) - as not representing any racist intent (i.e. //#​notracist//​) and //​justifying//​ this disaffiliation with racism because the tweeter is stating what they argue is a defensible or observable everyday truth (e.g. //#​justsayin//​). This gives the '​Truth'​ cluster map (fig. 3) its distinctive outer density pattern - the wide variety of largely non-associated terms appear as distantly related from other, due to their lack of co-occurrence together. ‘Truth’ as a type of racialized talk relies on a diverse array of largely single-use hashtags which users draw on not to participate in wider debate or discussion, but to make explicit their semantic intentions (however misplaced or ignorant).
 \\ \\
 \\ \\
multi-hashtags.txt · Last modified: 11-Apr-14 12:27 by sanjay