MISOGYNY ON TWITTER
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1. or whore It would be tempting to conclude on this basis that stories in the media are driving use of words slut and whore but this conclusion is hard to sustain as there are many examples where surges in reporting of rape on Twitter are not matched shortly after by a rise in the number of tweets with slut or whore in them eg January 22 January 24 January 27 January 29 January 31 and February 2 Thus it may be that in certain cases rape coverage is met with tweets using the words whore and slut but generally it seems that unusual use of such words is responding to other kinds of events such as television programmes like Celebrity Big Brother 13 Misogyny on Twitter TECHNICAL ANNEX Classifiers make use of natural language programming NLP in order to distinguish between different types of tweets The performance of all the classifiers used in the project were tested by comparing the decisions that they made against a human analyst making the same decisions about the same Tweets Classifier training involved for each classifier the creation of a gold standard dataset containing around 200 Tweets annotated by a human into the same categories of meaning as the algorithm was designed to do The performance of each classifier could then be assessed by comparing the decisions that it made on those 200 Tweets against the decisions made by the human analyst There are three outcomes2. 10 20 39 Jan150500 Jan151533 Jan172306 Jan200640 Jan221413 Jan242146 Jan270520 Jan281255 Feb 02 20 3 Power law analysis was conducted on the conversational data set There were 76 673 unique users in this data set Extensive use of these words was confined to a small minority of users 78 per cent of users tweeted either slut or whore once 14 per cent twice 4 per cent four times The user who produced the most tweets containing these words tweeted 415 times Figure 7 Power law analysis of use of the words slut and whore in conversation tweets Series1 1 78 Tweeters Series1 2 14 Series SH beller Sots 6 0 Tweets 11 Misogyny on Twitter CASE STUDIES WHAT DRIVES TRAFFIC Case Study 1 Celebrity Big Brother Increases in the use of sexist language can be driven by media events related to sexism and gender discourse For example the spike in the use of both slut and whore by both genders over 11 January was as a result of the contestant Dappy arguing with Luisa Zissman over the sexual promiscuity of men and woman and dual standards on Celebrity Big Brother Figure 8 Tweets containing the word slut rondappy dappy 200 f 40 N A Al V AA LA N Pand AZ Amf va aM AAT AAYW YN VN Wy SS EOE Jan 12 01 26 Jan 12 04 13 Jan 12 08 4 9 Jan 11 12 16 Jan 11 14 20 Jan 11 17 06 Jan 11 1953 Jan 11 2240 An analysis of the tweets containing the words slut and
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5. is likely to comment regardless of its low comparison to the test data set Low scores of precision recall and f score for this category probably arise from there being so few examples of comment tweets Table 3 Precision recall and F score for each study u em mm me Rape Slut whore 15 Misogyny on Twitter Demos Licence to Publish The work as defined below is provided under the terms of this licence licence The work is protected by copyright and or other applicable law Any use of the work other than as authorized under this licence is prohibited By exercising any rights to the work provided here you accept and agree to be bound by the terms of this licence Demos grants you the rights contained here in consideration of your acceptance of such terms and conditions 1 Definitions a Collective Work means a work such as a periodical issue anthology or encyclopedia in which the Work in its entirety in unmodified form along with a number of other contributions constituting separate and independent works in themselves are assembled into a collective whole A work that constitutes a Collective Work will not be considered a Derivative Work as defined below for the purposes of this Licence b Derivative Work means a work based upon the Work or upon the Work and other pre existing works such as a musical arrangement dramatization fictionalization motion picture version sound recording art repr
6. of this test and each measures the ability of the classifier to make the same decisions as a human and thus its overall performance in a different way e Recall This is number of correct selections that the classifier makes as a proportion of the total correct selections it could have made If there were 10 relevant tweets in a dataset and a relevancy classifier successfully picks 8 of them it has a recall score of 80 per cent e Precision This is the number of correct selections the classifiers makes as a proportion of all the selections it has made If a relevancy classifier selects 10 tweets as relevant and 8 of them actually are indeed relevant it has a precision score of 80 per cent e Overall or F All classifiers are a trade off between recall and precision Classifiers with a high recall score tend to be less precise and vice versa F1 equally reconciles performance and recall to create one overall measurement of performance for the classifier Generally classifiers worked well It was only for the second classifier distinguishing between comment and conversation for 14 Misogyny on Twitter the slut whore data set that there was low scores on one of the categories Given the strong performance of the other category in this classifier we are confident that this classifier is at least successfully classifying those uses of slut and whore that are conversational Thus anything else
7. relevancy classifier was then trained on the data in order to weed out all irrelevant tweets eg tweets referring to rape seed oil Once irrelevant tweets had been filtered out we were left with 108 044 relevant tweets Figure 1 Use of the word rape on Twitter conversation media vl Il W Mava Wh In Dec fe 02 18 Dec 31 1426 Jan Jan 172306 Jan 23 18 00 Jan 29 12 53 Feb04 0740 Fedor A classifier was trained to distinguish between tweets that were reporting or discussing stories about rape in the media and use of the word rape that were more conversational ie people discussing rape using the word colloquially making threats telling sick jokes etc 27 360 of this sample were media related and around 80 000 were conversational Based on the above graph it is clear that media coverage and general discourse parallel each other closely This suggests that media coverage tends to spark a broader discussion Misogyny on Twitter How is it used Based on a manual analysis of 500 randomly selected cases we found the following split in use types Table 1 Types of usage of the word rape on Twitter z Extrapolated weekly Type Proportion Amber Example tweet Serious news 40 4396 That was my famous rape face LOL Joke Metaphor casual 29 3187 Barcelona Vs Celtic should not be shown on television as a football game but rather as rape can I rape you please you ll like it T
8. MISOGYNY ON TWITTER Jamie Bartlett Richard Norrie Sofia Patel Rebekka Rumpel Simon Wibberley May 2014 DEMOS Misogyny on Twitter Open Access Some rights reserved As the publisher of this work Demos wants to encourage the circulation of our work as widely as possible while retaining the copyright We therefore have an open access policy which enables anyone to access our content online without charge Anyone can download save perform or distribute this work in any format including translation without written permission This is subject to the terms of the Demos licence found at the back of this publication Its main conditions are Demos and the author s are credited This summary and the address www demos co uk are displayed The text is not altered and is used in full The work is not resold A copy of the work or link to its use online is sent to Demos You are welcome to ask for permission to use this work for purposes other than those covered by the licence Demos gratefully acknowledges the work of Creative Commons in inspiring our approach to copyright To find out more go to www creativecommons org DEMOS PARTNERS CREDITS Prepared for Women of the World Published by Demos 2014 Demos Some rights reserved Third Floor Magdalen House 136 Tooley Street London SE1 2TU T 0845 458 5949 F 020 7367 4201 hello demos co uk www demos co uk Misogyny on Twitter INTRODUCTION Misogyny online is an i
9. com navigation health and behavior women arent welcome internet 72170 Usq9QZi5wZA twitter http www dailymail co uk tvshowbiz article 2537514 Dappy Luisa Zissman foul mouthed battle Celebrity Big Brother html 18
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11. hreat abusive 12 1319 Other 27 2967 De ee Nirvana Who is using it Over the time period there were 49 669 unique users contributing to the conversation data set Of those users men use the word rape more than women although it is not a significant difference Based on a random sample of 381 user profiles of people who tweeted as part of the non media related conversation about rape we found that 4 per cent of users made some reference to gender related activism 2 per cent appeared to be overtly sexist 9 per cent expressed some kind of maladjustment or anti social sentiment 8 per cent mentioned sports 10 per cent mentioned politics in some way and 12 per cent mentioned music Misogyny on Twitter Figure 2 Use of the word rape in conversation tweets by gender 600 500 400 29 Dec 2602 18 Dec 280306 Dec 30 10 40 Jan O1 18 13 Jan 04 01 46 Jan 06 09 20 Jan 08 16 53 Jan 11 00 26 Jan 130500 Jan 15 62 18 Power law analysis was carried out on all conversation tweets making reference to the word rape in order to establish how frequency of use was distributed among users 79 per cent of users tweeted only once 12 per cent twice 4 per cent three times The most prolific tweeter of rape tweeted 392 times Figure 3 Power law of use of the word rape in conversation tweets Series1 1 79 Tweeters Series1 2 12 j 9 Seriesh Zet Shear Selikor Gets 7 0 Ser
12. iest 18 1 weets Misogyny on Twitter STUDY 2 USE OF THE WORDS SLUT AND WHORE Volume of use Our initial search produced 6 001 865 tweets which we filtered down to 161 744 coming directly from the United Kingdom A relevancy classifier was then trained to remove all irrelevant tweets leaving us with 131 711 tweets that used the words slut and whore 48 006 contained the word whore 85 204 contained slut Figure 4 Use of the words slut and whore on Twitter whore siut 4392 4000 o Jan 09 20 35 Jan 130800 Jan 15 15 33 Jan 17 23 06 Jan 200640 Jan 22 14 13 Jan 24 21 46 Jan 2705 20 Jan 29 12 53 Feb 02 In the rape data set there were a significant proportion of media stories being shared which was not the case in the slut and whore data sets Therefore using the same technique as study 1 we automatically split the data into comment tweets which were about the use of word itself and conversation tweets which included the word as part of a conversation We found 7 993 tweets that were commenting on usage of these words 108 409 that were actual conversational usage In a similar way to the rape tweets the broad pattern of traffic is relational a small number of comment tweets to correlate with a wider set of conversations However the causal relationship is not clear Misogyny on Twitter Figure 5 Comment and conversati
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14. n depth piece for Pacific Standard in January 2014 3 Since then the subject has provoked significant debate and discussion about the extent of misogyny on line and on Twitter in particular and what might be driving it To give a rough and ready illustration we ran a series of short studies in order to better understand the volume degree and type of misogynistic language used on Twitter Misogyny on Twitter Methodology In study 1 we collected all tweets in the English language which included the word rape over the period 26 December 2013 9 February 2014 all of which from Twitter accounts based on the UK In study 2 we collected all tweets in the English language which included a series of terms that are broadly considered to be used in a misogynistic way over the period 9 January 4 February 2014 all of which were from Twitter accounts based in the UK In this analysis we only include tweets which contained the words slut and whore which were by far the most voluminous We subjected each data set to a number of analyses using both qualitative and quantitative methods 1 Volume over time 2 Different types of use 3 Who is using these words 4 Case study what drives traffic All tweets were publicly posted and collected using the public Twitter Application Programming Interface API To conduct the analysis we conducted both automated analyses using a technique called natural language
15. ncreasing worry According to authors such as Ellen Spertus who first wrote about fighting online harassment in 1996 Jill Filipovic and Pamela Turton Turner the online space has long been a difficult place for women to operate While the internet was seen as a utopian platform for free speech and equality when it began to become popularly used in the 1990s it was evident from the very start that the inequalities that structured real world society had been transferred online Research has consistently found that women are subjected to more bullying abuse hateful language and threats than men when on line According to the Pew Research Centre s 2005 report How Women and Men Use the Internet an 11 per cent decline in women s use of chat rooms stemmed from menacing comments Meanwhile researchers from the University of Maryland set up a host of fake online accounts and then sent these into chat rooms Accounts with feminine usernames received an average of 100 sexually explicit or threatening messages per day whereas masculine names received 3 7 2 The subject was propelled into the public consciousness in the summer of 2013 when a number of prominent female journalists and activists in the UK were subjected to a sustained series of violent rape and bomb threats from Twitter users Following these incidents Amanda Hess and American writer documented her own experience of receiving rape threats from Twitter users in an i
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17. on uses of slut and whore 485 450 400 350 300 250 200 150 100 50 Anni Jan 09 20 39 Jan 11 0026 Jan 12 04 13 How are they used Based on a manual analysis of 500 randomly selected cases we found the following split in use types Jan 13 08 00 comment conversation ai arn Yad nt a N nt ne Jan141146 Jan1515 33 Jan161920 Jan 1723 06 Jan 19 18 5 Table 2 Types of usage of misogynistic words on Twitter Type Proportion Extrapolated weekly number Example Serious non offensive 10 2710 Slut shaming by man with history of abuse the norm Young girls backing him up on here fucking despair cbbuk bbbots Colloquial casual 35 9486 XXX relpy to my texts you slut LOL if i was pretty and skinny would be such a whore oh my god Generally misogynistic 18 4878 Why take photos lookin like a slut and then moan when people say bad things You bought hate upon yourself and you know it Abusive 20 5420 XXX XXX You stupid ugly fucking slut I ll go to your flat and cut your fucking head off you inbred whore Other inc subversive and porn 16 4336 Slut dropping in the shower to pick up the shampoo 10 Misogyny on Twitter Figure 6 Use of the words slut and whore by gender conversation tweets DE M 2700 2500 2500 2400 2300 2200 af je f SN IN 5 1800 1715 Jan
18. processing and qualitative analysis where a researcher carefully reviewed random samples of the data Key findings Between 26 December 2013 and 9 February 2014 there were around 100 thousand instances of the word rape used in English from UK based Twitter accounts We estimate around 12 per cent appeared to be threatening Misogyny on Twitter Women are as almost as likely as men to use the terms slut and whore on Twitter Not only are women using these words they are directing them at each other both casually and offensively women are increasingly more inclined to engage in discourses using the same language that has been and continues to be used as derogatory against them Between 9 January and 4 February 2014 there were around 131 000 cases of slut and whore used in English from UK based Twitter accounts We estimate that approximately 18 per cent of them appears misogynistic There was a high proportion of casual misogyny Approximately 29 per cent of the rape tweets appeared to use the term in a casual or metaphorical way while approximately 35 per cent of the slut and whore tweets appeared to use the term in a casual or metaphorical way Misogyny on Twitter STUDY 1 USE OF THE WORD RAPE ON TWITTER Volume of use Our initial search produced 2 725 097 tweets using the word rape When limiting this to tweets from only the UK this number fell to 138 662 A
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20. whore on January 11 showed a significant volume of tweets referring to Dappy as the Celebrity Big Brother argument takes place and is commented on With almost immediate effect discussion about the argument drives a short term more general increase in the use of the terms slut and whore on Twitter which continues for some time after the direct discussion of Dappy ends Case Study 2 Rape news stories versus slut and whore conversations Reporting of rape related stories in the media via Twitter is greatest from 22 January onwards This period coincides with some high profile celebrity rape trials On 19 January there is a spike in the number of tweets about rape in the news that is followed by a relatively large spike in tweets containing the words slut or whore that is followed by another surge in rape news tweets 12 Misogyny on Twitter Figure 8 Rape reports in the media and use of the words slut and whore slut amp whore rapenews threshold Tweets ENERET NERI NN ll FT a LZ LRAD UEP AFP ala AP On 23 January there is a spike in the number of tweets referencing rape in the news that is followed not long after by an unusual number of tweets using the words slut or whore Also on 26 January there is another example of a surge in the number of tweets making reference to rape in the news that is followed by an unusual number of tweets using slut
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