Social vs. Armchair Activism with Twitter

Social vs. Armchair Activism with Twitter Studying the propagation of activism through Twitter using the case study of rape incidents in India

Abstract

This paper aims to examine if and how Twitter can be used as a tool to propagate social activism. This is done

by studying the tweets related to the rampant rape incidents in India that occurred specifically in the year 2013.

This research examines if Twitter as a social media platform helps in rallying support for social causes, and what

kind of awareness can be generated through this medium. There are a total of 1.9 million tweets and over 900

thousand distinct users during this period. By sub-dividing this huge data set into specific time periods and a

close analysis of thousands of individual tweets, I have found that in this instance, Twitter has mainly functioned

as a news breaking and update channel, rather than as a platform that brings about social change.

Sameena

Courtesy: Google Images

Background The latest crime statistics of India indicate that about 93 women in India get raped everyday. (The Times of India) In the year 2013 there have been over 33 thousand rape cases that have been reported in the country (Zee News), a signiDicant rise from the year earlier. It is suggested that one reason for this increase is that after the 2012 Delhi gang rape incident and the nation wide protests it spurred, people have become bolder in reporting more cases. Earlier, most of the would go unnoticed and unreported. The incidents in 2012 – 13, led to a large number of people protesting for days together against the Indian Government’s inaction to speed up the justice in rape cases, and also for bringing in stricter laws and security measures to deal with this problem. In the history of India, such a wide scale protest for a social cause was unprecedented, and this caught up in many cities around the country. The nation-‐wide protests eventually put a lot of pressure on the Indian judicial system, with the result that the verdict for 2012 case was given in a period of 9 months (one of the fastest verdicts in India), and the government also passed a new anti-‐rape bill with stricter punishments and guidelines for safety of women. All these events have put India on the radar, with people from all over the world following and discussing these events. Social media has played a huge role in disseminating information regarding the events worldwide. In this paper, I study the role Twitter plays in 3 ways: as a platform of disseminating news, as a platform for people to express their opinions on a given topic and as a platform to initiate awareness and action against these incidents (social activism). The aim is to study if the Twitter users are content with spreading the news updates and venting their frustration, or are they making use of this wide network of connections to initiate on-‐ground actions to Dight this evil. Before venturing further into the study, a brief timeline of the major rape incidents in India is shown below for the year 2013. This event timeline with the major rape-‐related news items of the year, is compared to the tweet timeline to check for inDluences and impact.

Figure 1 – Timeline of rape incidents in 2013, India

Studying Twitter As A Platform For Social Activism

Historically Twitter has been functioning in 3 different modes: Twitter I, II and III. (Rogers, 2013) Twitter I is more a lifestyle update platform where users update their friends’ circle about the current happening in their life, most of which made seem like a babble to the world at large, but makes sense to the user’s friends. Twitter II is a platform that people use -‐ to share, read, give quick updates, and even rally people for protests and uprisings, as in the case of Middle East (Rogers, 2013). This is also the age of #hashtags. Twitter in its 3rd generation “has settled into a data set, from which researchers have made collections, and one to be archived and made available by the U.S. Library of Congress.” (Rogers, 2013) In this paper, studying the rape incidents in India through Twitter falls under the ‘Twitter II’ category. By analysing the tweets and discussions around these incidents, this paper will study the platform’s usability as a tool to initiate social awareness and propagate on-‐ground activities of change. The Twitter data for this incident is extracted and analysed with the help of a tool called TCAT (Twitter Capturing and Analysis Toolset). The tool helps in analysing the data relevant to a speciDic time period and events only. By selecting the relevant sub-‐texts, and specifying the time periods, one is able to extract the data in various formats and styles. The tool gives a detailed breakdown of data in terms of user speciDications, #Hashtags, URLs, Retweets, complete tweet data etc. It also has a facility to export some of these data as networked graph for direct visual analysis and representation. At a meta-‐level, once the search query has been given, the tool also indicates the percentage of tweets that use external links in them. Also, there are 2 graphs that indicate the spread of the tweets in the data set over a given time period (be it hours, days, months or years). These quick data analysis snapshots indicate a good starting point for users to begin their research and analysis. Overall it is a great tool that helps capture and analyse Twitter data in one single platform. The tool does have a few limitations, and they will be discussed in the context of the research analysis further on in the report.

Research Question Against the backdrop of rape incidents in India, besides being an active channel of news-‐breaking and disseminating information, what role did Twitter play in helping raise social awareness and propagate on-‐ground social activism to Dight rape?

Scope Of Study This study concerns the incidents that are solely concerned with the year 2013. One of the major rape incidents, ‘Delhi gang rape’ that occurred in December 2012 is not included in this dataset, because TCAT has a pre-‐set data collection as a part of its repertoire. Each of these datasets are monitored and tracked for a speciDic period of time only. The data set “delhirape” that is used for this study was tracked from 15 January 2013 onwards. Hence I have no access to the tweets preceding this point of time. To keep the study comprehensive, while also allowing a scope for detailed analysis, the study is limited till 31 December 2013. Another point to mention here is the fact that TCAT has two separate datasets belonging to the broad query of “rape”. One is “delhirape” and the other is “rape”. My reason for not using “rape” is because this dataset captures tweets only from 27, November 2013 onwards, which does not make it a strong base to work with. Hence I made the choice of using “delhirape” as the dataset of this study. A last point of consideration is that there were over 33 thousand rape cases reported in India in 2013. Not all of them are covered by Twitter followers, nor are they all a part of this study. The event timeline considers only those incidents as a part of this study that has generated a high user activity on Twitter. This by itself is a massive dataset (close to a million tweets), and provides adequate scope to study the trends and analyse them.

Methodology The research methodology has two parts to it:

a. DeDining the query and selecting parameters b. Collection and consolidation of the data

I. De&ining the query and selecting parameters: It is very important to design the query accurately as it helps in acquiring pertinent information and excludes the non-‐relevant data. In this case, the main data set is ‘delhirape’, and it contains the pre-‐set keywords ‘delhi’, ‘delhirape’, ‘gangrape’ and ‘rape’. The keywords ‘rape’ and ‘gangrape’ are highly generic and broad. While they help accumulate news on rape incidents beyond the ‘2012 Delhi rape case’, they also open up the Dield to include news items from outside India as well. To avoid this situation, I have deDined a speciDic list of select parameters that will help curtail the dataset to within India. The sub-‐sampling parameters are: ‘india’ OR ‘delhi’ OR ‘mumbai’ OR ‘bangalore’ OR ‘UP’. The signiDicance of each of these keywords is as follows:

a. India – will limit the dataset to the news about India and the incidents happening there.

b. Delhi – this was re-‐speciDied as a cautionary measure. c. Mumbai / Bangalore – These are the places where some of the major rape

incidents of 2013 occurred. d. UP (Uttar Pradesh) – This is India’s biggest state and is also known in particular

for its high number of rape related incidents (both reported and unreported). Hence this has been included too.

As will be evident later on in the report, these keywords did manage to eliminate most of the unrelated data, but not all of it. And in a small way, did affect the accuracy of the results of this report. This is a limitation of the tool, which if corrected can help produce more accurate results.

The next select parameter to be speciDied was the date range: 01 January 2013 to 31 December 2013 (Attachment 1.zip). A snapshot of the query section is shown below:

Figure 2 – Snapshot of data selection window

The above Digure represents the query for an annual analysis. However, as the event timeline indicates in Figure 1, there are speciDic time periods when the on-‐ground news activity and the user activity on Twitter was very high. To get a more detailed view of these periods, I also collected the data for:

a. SpeciDic date ranges like: 01-‐31 January, 11-‐18 March, 20-‐23 April, 23-‐31 August, and 10-‐13 September. (Attachment 2.zip)

b. Individual dates like: 11 March, 20 April, 31 August, and 13 September. These were the dates with highest number of Tweets in the entire timeline, and a close study of these dates is important to get a feel of the trend. (Attachment 3.zip)

II. Collection and consolidation of data The TCAT tool as explained earlier, provides data in different formats. Keeping the scope of this study in mind, I have collected the following data formats for the yearly and date-‐range periods:

a. On a ‘per day’ basis (selection button on the TCAT window) i. Tweet Stats – to get an overall feel of the dataset

b. On a ‘overall’ basis (selection button on the TCAT window) i. Hashtag Frequency (minimum freq of 10) ii. Hashtag User Activity iii. User Visibility (mention) iv. URL Frequency (minimum freq of 10) v. Identical Tweet Frequency (minimum freq of 10)

c. Tweet Exports – with a speciDic mention of URLs and #Hashtags i. Random Tweets

ii. Full Set (Attachments 1,2 have theses above-‐mentioned respective datasets included in them)

For the individual dates, just the hashtag frequency and the URL frequency were collected (Attachment 3), as a means of cross-‐referencing with the overall analysis. What was most interesting from this data set were the graphs generated on the TCAT page, which helped determine the mood of the twitter users. (As shown in the next section)

The data collected was then consolidated in the following manner:

i. DeDining Tweet topics – the various topics / events related the tweets in the data set cover. The tweets were scanned for speciDic keywords witht he issue background in mind. 6 different topics emerged from the tweet analysis: a. Delhi Gang Rape – Keywords: Jyothi Singh, delhi, delhi gang rape, bus,

nirbhaya, india b. Mumbai Gang Rape -‐ Keywords: Mumbai, photo journalist, Mumbai gang

rape, goregaon, india c. Delhi Minor Rape -‐ Keywords: delhi, delhi rape, delhi minor rape, 5 year

old, minor rape, child, india d. Activism Enablers -‐ Keywords: denimday, 1 billion rising, stop rape, rape

culture, sexual assault, VAW (violence against women), boycott, shame, human rights, gender violence, harassment, avoid rape, verma report, anti-‐rape, anti-‐rape law, anti-‐rape bill, outcry, outrage

e. Other Rape Incidents -‐ Keywords: aasaram, bapu, tehelka, tejpal, swiss tourist, MP, rape, gang rape, india

f. Non-‐related topics – No keywords. Tweets that do not relate to any of the ‘rape incidents’ in India. These are tweets that may be related to rape but in different country, or have no connection to rape at al and are just stray tweets captured by the tool.

ii. DeDining type of user – The entire twitter user base for this dataset can be divided into the following categories: a. Popular – users with large fan base and following b. Individual – users with not a high fan following c. News / Media channel – organisations of news and media channels

iii. Top #Hashtags – how much and which #hashtags have been the most popular

iv. Top URLs – how much does the data set refers to external sources, and which are the popular ones

v. Most InDluential Users – which users have received maximum mentions and for what type of tweets

vi. Top Retweets overall – which tweets have been most popular, and to which type they belong.

vii. Top Retweets related (to the scope of study) – since the data collected had a lot of tweets that did not relate to the scope of the study, for this category I considered it important to know which tweets and which type were most popular within the scope of the study.

A detailed analysis of these will be found in the next section.

Analysis & Inference The analysis and inference derivation of the data collected can be split into 3 parts:

a. Overview b. Top categories c. User interactions

I. Overview In this part we analyse the 2 graphs that the TCAT page develops for the given parameters. This analysis is split 2 ways:

a. Annual overview: From Digure 3, it is evident that for the entire time period of this study there have been over 1.9 million tweets from over 900 thousand distinct users! As is evident from the pie chart, majority of these tweets do not refer to any external source for further information or stories, which indicates that the news update on Twitter was good enough to tell the whole story. (Validated further)

Figure 3 – Overview of annual selection

The timeline of the tweets in Digure 4 shows some prominent peaks with the rest of the year maintaining a low steady trend. On a closer examination, it is evident that the time periods of these peaks coincide with some major ofDline developments in the rape scenario, as shown in Digure 1. At the meta-‐level, it seems like the Twitter audience was mainly using the platform to follow and discuss these major milestones or news updates, while during the other times the interest level in this topic was considerably low.(Validated further)

Figure 4 – Overview of the annual tweet timeline

b. SpeciDic date range overview: The analysis of the data collected for 01-‐31 January, 11-‐18 March, 20-‐23 April, 23-‐31 August, and 10-‐13 September is shown in Table 1. Figures 5 & 6 show the visual representation of the same. This table also combines the analysis of major dates like 11 March, 13 Sept etc.

Type of analysis

01-‐31 January (*Data available only from 14th onwards)

11-‐18 March

20-‐23 April

23-‐31 August 10-‐13 September

Total number of tweets

143.547 123.221 82.822 461.463 96.747

Total number of distinct users

83.455 69.164 49.434 265.372 55.206

External source reference (no. of links)

A little over half the tweets refer to an external source link -‐ 55.8%.

About half the tweets have an external source link -‐ 50.9%.

About half the tweets have an external source link -‐ 51.5%.

A very small percentage of tweets refer to an external source link -‐ 30.6%

A little over half the tweets refer to an external source link -‐ 61.4%.

Tweet activity analysis

There is a consistently high tweet activity in the entire period, with most days having over 9 thousand tweets. The 2012 December Delhi rape incited a lot of strong reactions from public, which seems to have spilled over in January too. (Validated further in the section)

This period has the highest tweet activity in the history of the timeline. It sees a drastic rise and drop in the tweets, indicating that the events of 11 March and 18 March were of most interest to the Twitter users. The events they relate to are the death of the main accused in Delhi rape case, and passing the anti-‐rape bill respectively. Also a good point to note here is that the death of the accused caused a lot of stir on Twitter, but in comparison an even more important event, anti-‐rape law, did not garner that much attention. This proves to a large extent that users on the platform are not really interested in ‘change-‐initiation’ process. (Validated further in the section)

This period sees a high number of twitter activity throughout. A study of the event timeline indicates that this was the time of the second Delhi rape case in which a 5 year old child was assaulted. There were a large number of protests all over the country. It was during this time that the juvenile accused in the 1st Delhi rape case was sentenced. Dates 20 & 22 respectively. Here too the tweet activities show preference of sensationalism over social cause. The day of the rape incident sees high furore on Twitter, which immediately dies down in a day. And when there is another news the activity picks up again. (Validated further in the section)

This period sees a constant trend of tweet activity. As per the event timeline, this was the period of the Mumbai gang rape case, an incident that sparked rage and protests through out the country. The tweet timeline indicates an appetite for event updates rather than focus on enabling a change. (Validated further in the section) The last peak in the timeline is when one of the accused in the Mumbai rape case was arrested.

Like March, even this timeline reDlects the drastic rise and drop in interest according to the sensationalism of the news. On 10 Sept. the court Dinds all the accused of 1st Delhi rape case guilty. And on the 13th they are all sentenced to death. The activity again shows the preference of sensationalism over the social cause. This period unlike the earlier ones, also has a high number o external source reference, most of them leading to news updates on the verdicts. (Validated further in the section)

Table 1: Analysis of monthly data

Figure 5 – Overview of the tweets with links for different periods

Figure 6 – Overview of the tweet timelines for different periods

From the above analysis, and the graphs it is clear that the Twitter users in this issue space are mostly interested in the sensational news items (like death sentences) and not on topics that relate to bringing about social change (like the anti-‐rape law). While the public attention to these major incidents is valid, Twitter is a platform to build networks and social connections. A lot more solidarity and social unity can be achieved through such connections, instead of just passing around news updates within the circle, and condemning the activities from the comforts of an armchair. (Validated further)

II. Top Categories As explained above in the methodology section, the different data forms were consolidated to arrive at the following analysis:

1. Top #Hashtags:

Table 2: List of top #hashtags

¥ 80 percent of posts in the dataset don’t use #hashtags. A study of the sample

1000 random tweets of each category will show this. (Attachments 1/2) ¥ Only those with more than 10K frequency are considered in the top list – There

are 9 of them in all. ¥ Of these, 6 are the keywords of the main query and are hence well associated

with the subject of the research. ¥ Of the other three, one is a news update #hashtag, one about a rape scandal

related to a godman (sensationalism), and the last one is an activism based #hashtag (VAW -‐ violence against women)

Hashtag Frequency Remarks

India 84087 Part of the query

rape 82531 Part of the query

Delhi 21718 Part of the query

News 17871 News updates by channels and media houses

gangrape 15727 Part of the query

Asaram 14734 rape scandal of godman

DelhiGangRape 14286 part of the query

vaw 11349 Violence against women

DelhiRape 10021 part of the query

As is evident from the table, events related to the 1st Delhi Gang rape seem to be trending the most. News updates on the incidents and any sensational scandals garner far higher popularity, than messages with social change.

2. Top URLs

URL Frequency Type of URL Remarks

http://nationalreport.net/assam-‐rape-‐festival-‐india-‐begins-‐week/

5417 News / media Independent news source. Partially activist

http://www.womenspowerbook.org/sexualrevolution-‐india-‐rape-‐harassment-‐freedom-‐womenpower-‐

book-‐empowerment.htm#.UL

vU5Wf77qw

3816 Activist group

http://modernworldhub.blogspot.co.uk/2012/12/sexual-‐revolution-‐plus-‐

joyous-‐or.html

3018 Blog / activism

http://womenspowerbook.org/articles/state-‐of-‐indian-‐state-‐scant-‐

thinkers-‐philosophers-‐corruptions-‐epidemics-‐dalits-‐solving-‐making-‐world-‐leader-‐india-‐rape-‐parliament-‐

india-‐1.htm#.UecR9211eE8

2559 Activist group

http://www.theawl.com/2013/07/rape-‐joke-‐patricia-‐lockwood

1989 Blog / activism Poetry to convey message on rape

http://www.urbandictionary.co

m/deDine.php?term=no&deDid=125361

1952 ActivismA page that deDines the meaning of "no" as a

word

http://focusghana.com/the-‐assam-‐rape-‐festival-‐in-‐india-‐begins-‐this-‐

week/

1717 News reporting site for Ghana Page no longer exists

http://news.sky.com/story/1167890/indias-‐top-‐cop-‐under-‐Dire-‐for-‐

rape-‐remarks

1666 News / media Focus on a scandal

Table 3: List of top URLs

¥ Since the entire dataset has 5000 or less as its frequency, I have considered top

10 URLs for the study here ¥ It is interesting to note that of the top 10, 5 of them are activistic in nature.

However considering the frequency associated with each of these URLs it is indicative that they haven’t been very popular with the audience.

¥ There are 4 news or media channel URLs as well, and one with an unrelated

video. ¥ As seen in Figure 5 and Table 1, tweets with URLs or external sources are not

very dominant in the entire dataset. Hence, even though there have been attempts to bring up some social messages in the pool by organisations / individuals it hasn’t been too well received. Thus validating the point made earlier that users are mostly interested in sensationalism and news updates.

http://www.youtube.com/

watch?v=o_Z2iq4DnXg&feature

=youtu.be

1614 Video A humorous video on rape

http://www.ndtv.com/article/india/delhi-‐gang-‐rape-‐case-‐main-‐accused-‐ram-‐singh-‐allegedly-‐commits-‐suicide-‐in-‐tihar-‐jail-‐340752

1425 News / media News report

3. Most InDluential Users:

User Frequency of mention Type of user User details Sample tweet Remarks

MJJPEACE 20361 Popular Japanese user

@MJJPEACE Delhi Gang Rape: Teenager Found Guilty: The victim's mother who had pushed for the death penalty leaves court... @bieberrkfc

Both the users tag each other -‐ and their tweets are all news updates of the rape incidents as they unfold.. Get over 200 retweets on an average

bieberrkfc 19206 Popular Russian user

@bieberrkfc Second man held over Mumbai rape: A second man is arrested over the gang-‐rape of a 22-‐year-‐old photo journalist... @MJJPEACE

ndtv 16346 News Channel News media channel

rt @breakingnews: main accused in new delhi gang rape case commits suicide in jail ofDicials say -‐ @ndtv http://t.co/aaxen4oixk http://bit.ly/y3sntu

News updates on the different incidents -‐ links to their website for more live news. An average of 150 retweets

Table 4: List of most in\luential users

¥ There are 5 users with more than 10k frequency, of which 2 of them are news

media channels. ¥ 3 of them are popular users with large fan bases, but do not belong to India nor

does their fan base. Their main messages are news updates on the incidents. ¥ Besides Indian News channels, none of the other popular twitterati from India

Digure anywhere in the top list. From the above table, it is clear that those twitter users who have been reporting the incidents have high popularity. However, the inDluencers do not use the opportunity of their large fan base to propagate messages for social change. They are all content with reporting the news, and condemning the incidents. (refer to the datasets of user mentions for any time period)

timesoDindia 13200 News Channel News media channel

RT @timesoDindia: Delhi gang-‐rape case: Death sentence for all four convicts http://t.co/s3xStvO47M [[http://timesoDindia.indiatimes.com/india/Delhi-‐gang-‐rape-‐case-‐Death-‐sentence-‐for-‐all-‐four-‐convicts/articleshow/22547783.cms?utm_source=twitter.com&utm_me

news updates on the different incidents -‐ links to their website for more live news. An average of 50 retweets

BadKidAndrew 10817 Popular

User that is tagged in all posts of @stfudustin

@stfudustin : india ink: india rape suspect found dead in jail: ram singh one of Dive men accused in the fatal delhi a... @badkidandrew

stfudustin is suspended from twitter, and badkid operates through another private twitter channel

4. Top Retweets

Table 5: Top Retweets overall

Retweets Frequency By user Type of user Type of tweet

RT @DrunkKane: Just tried to submit our last two goals on PornHub but they still wont accept rape... #2Goals1Cup

4899 @DrunkKane Popular unrelated

RT @BrooksBeau: What my kid would look like of I was to rape a kangaroo http://t.co/rTMffH9uPw

4207 @BrooksBeau Popular unrelated

RT @UberFacts: To maintain control of their groups powerful male dolphins will occasionally rape the weaker males.

3664 @UberFacts Popular unrelated

rt @kevinhart2reai: nba news: deandre jordan arrested during halftime. currently being brought up on charges of rape & aggravated as ...

2777 @kevinhart2reai Individual User unrelated

RT @BestProAdvice: Seen on a newspaper in India a country with one of the highest rates of rape and sexual abuse http://t.co/Q3YObsrGpY

2283 @BestProAdvice Popular news report

RT @julianperretta: Julian shooting in Dehli India. 'Thats All' video http://t.co/7Nvks8rvfH

2108 @julianperretta Popular unrelated

RT @TipsForYouDaily: Seen on a newspaper in India a country with one of the highest rates of rape and sexual abuse http://t.co/PNLKiYSMeg

1810 @TipsForYouDaily Popular news report

RT @TriciaLockwood: I have a poem called ""Rape Joke"" up at The Awl today. It is a serious poem: http://t.co/xZ9sUDG0R5 [[http://www.theawl.com/2013/07/rape-‐joke-‐patricia-‐lockwood]]

1634 @TriciaLockwood Popular activism

rt @chrislhayes: call me crazy but i think the next pope should be someone who didn't help cover up child rape.tho that may disqualify ...

1476 @chrislhayes Popular unrelated

rt @uberfacts: to maintain control of their groups powerful male dolphins will occasionally rape the weaker males.

1408 @uberfacts Popular unrelated

Retweet Frequency By user Type of user Type of tweet

RT @BestProAdvice: Seen on a newspaper in India a country with one of the highest rates of rape and sexual abuse http://t.co/Q3YObsrGpY

2283 @BestProAdvice Popular News report

RT @TipsForYouDaily: Seen on a newspaper in India a country with one of the highest rates of rape and sexual abuse http://t.co/PNLKiYSMeg

1810 @TipsForYouDaily Popular News report

RT @SrBachchan: T 1334 -‐ Seen on a newspaper in India a country with one of the highest rates of rape and sexual abuse http://t.co/esjXRytâ€¦

902 @SrBachchan Popular News report

RT @ashramindia: Girl said = ""Rape not done""! Police Said = ""Rape not done""! #PaidMedia said= ""#Asaram Bapu did Rape!"" A #MahaQuiz 4 #Indiaâ€¦

831 @ashramindia Popular News report

RT @ashramindia: ""UNPROVEN"" case of #Asaram bapu is a ""National Issue! But a PROVEN RAPE of a girl is just an ""Internal Matter"" 4 #Shoma Châ€¦

789 @ashramindia Popular News report

Table 6: Top Retweets related

RT @cnnbrk: 4 Indian men convicted in the rape and murder of a 23-‐year-‐old woman have been sentenced to death. http://t.co/3ZmVcZmBai [[http://on.cnn.com/18YRfrp]]

786 @cnnbrk News Media News report

RT @Know: Seen on a newspaper in India a country with one of the highest rates of rape and sexual abuse http://t.co/9dSfdr6Ebz

781 @Know Popular News report

RT @SkyNews: ""If you can't prevent rape you might as well enjoy it "" says India's police chief: http://t.co/IczVUjo9CQ [[http://news.sky.com/story/1167890/indias-‐top-‐cop-‐under-‐Dire-‐for-‐rape-‐remarks]]

756 @SkyNews News Media News report

RT @Pink: â€œ@nytimes: Reports of Rape of 5-‐Year-‐Old in India Set Off Furor http://t.co/6dBOkun1f5â€ AS IT SHOULD. DISGUSTING ANIMALS [[http://nyti.ms/15qck0L]]

590 @Pink Popular News report

RT @Gayatritwit: The whole India wants to know why Rape happens. This is why it happens. Tweet to me by Rapist shameless @kamaalrkhan http:â€¦

550 @Gayatritwit Popular Activism

¥ This analysis is a clear indication of just how broad / under-‐speciDied the main

query data set is. Even adding sub-‐sampling parameters, has not eliminated all the unrelated data

¥ Of the top 10 retweets in Table 5 (the frequency of retweets is not very high, less

than 5k) 7 of them are unrelated to the topic in question. Of the related ones, 2 of them are news reports on the incidents. One is an activism related tweet, but its relevance to India events cannot be determined. Hence to be able to make an accurate analysis, I have extracted the top retweets relating to the topic from the data set.

¥ A look at this data set (table 6) shows us that 7 are news reports, and one tweet

with a very low frequency of about 500 is related to social activism. But the fact that it did not gain too much popularity goes to prove our point further that activist related tweets didn’t gather any support during this period.

All these above data points are cross-‐referenced with the datasets generated for individual dates (Attachment 3.zip) and they validate the inferences drawn.

III. User Interactions This section is a Dinal proof of all the inferences derived earlier in the section. It shows the connections between tweet activity and the topics discussed.

a. Interactions during March & September

Figure 7 – Interactions during March & September

March September

Figure 8 – Distribution of tweet topics during March & September

• From the pie charts it is clear that both the months show a dominance of news

based on 1st Delhi rape case. • In Digure 7 the peaks clearly indicate the days that had big news updates on 1st

Delhi rape case (refer Digure 1) • What is also interesting to see is that the peaks occur when the news breaks in,

and the rest of the time there is low activity. This is indicated by both date wise and month wise graphs.

• Inference is that the Delhi rape case and events related to it are most popular

topics in this database, and the interest level is limited to the news in question.

b. Interactions during January, April & August

Figure 9 – Interactions during August, April & January

Figure 10 – Distribu/on of tweet topics during August, April & January

• From the pie charts it is clear that the months show a dominance of news-‐based

topics, Mumbai rape case etc. In August, there were a particularly large number of unrelated tweets, and hence can be ignored.

• The pie charts also indicate that when a new topic is trending it ignores the other

topics that may have been hugely popular earlier. • In Digure 9 the tweet activities indicate a steady Dlow of tweets till the topic is hot

news, and then they decline. • Inference is that the most popular topics in this database are the current news

updates and incidents related to rape.

This set of analysis determines the mood of the user and popularity of topics amongst the users. This analysis functions as a Dinal proof of all the earlier analysis that were made and is summarised in the next section.

January

August April

Summary & Conclusion In conclusion, the answer to the research question I posed earlier, “Against the backdrop of rape incidents in India, besides being an active channel of news-‐breaking and disseminating information, what role did Twitter play in helping raise social awareness and propagate on-‐ground social activism to \ight rape?” is as follows: Twitter’s role in raising awareness about a certain issue has been strong. The quality of the inDluencers in the dataset is good with large fan base and following. These kind of users tweeting and talking about rape incidents and condemning them instigates their followers to also take notice of what is happening, and if not tweet an individual post at least to retweet the inDluencer. However, as evident from the analysis above, users (both inDluencers and individual users) generally have limited themselves to parroting the news updates, and condemning the incident. None of the major inDluencers indicate any kind of interest in initiating social change. While all of them tweet about stopping it / Dighting it, there’s hardly a voice that talks about how this change can be brought about. A small number of organisations / individuals or NGOs do tweet about joining their communities to Dight this evil, but by seeing their frequency numbers, it is evident that they haven’t reached a large set of audience and their ideas (good / bad) do not resonate with them either. This sufDiciently proves that Twitter in this instance, has solely performed the role of a news-‐breaking and information dissemination channel. It does not function as a platform that brings about social change or a revolution using its network of connections. The biggest learning from this episode, is that noise on social media platforms does manage to raise some awareness about a certain issue. But it does not always imply or bring about a positive action / change. Sometimes noise is just what it is; noise -‐ created by users whose main objective is being heard. But as the theory of Web 2.0 states, the social media platforms exist for and because of the users (O’Reilly, 2009). So the platform by itself cannot bring about a positive change, the users need to. And that will happen when the users understand the true potential of these platforms they work on and put it to the right use. Right now these platforms just seem like an easy way for people to be ‘part of’ a revolution, without actually being part of it.

Going Forward: This paper is an attempt to examine new media platforms and their role in driving social change through studying critical social incidents. This study can be further applied to other social incidents or revolutions around the world, to check how the

new media platforms fare in those instances. An overall comparison of these different events can help in a better understanding how people are using these platforms, and what can be done to ensure that the full potential of the platform and its network of connections is put to the right use.

References

• “33,707 Rape Cases Registered in India in 2013, MP Tops List | Zee News.”

N.p., n.d. Web. 4 Dec. 2014.

• “93 Women Are Being Raped in India Every Day, NCRB Data Show -‐ The

Times of India.” N.p., n.d. Web. 4 Dec. 2014.

• “Rape In India: Why It Seems Worse | TIME.com.” N.p., n.d. Web. 4 Dec. 2014.

• R. Rogers (2013). "Debanalizing Twitter: The Transformation of an Object of Study." Proceedings of ACM Web Science 2013. Paris: May 2013.

• Tim O’Reilly (2005). ‘What is Web 2.0,’ O’Reilly Media

Appendix URLs of news articles http://zeenews.india.com/news/nation/33707-‐rape-‐cases-‐registered-‐in-‐india-‐in-‐2013-‐mp-‐tops-‐list_944636.html http://timesoDindia.indiatimes.com/india/93-‐women-‐are-‐being-‐raped-‐in-‐India-‐every-‐day-‐NCRB-‐data-‐show/articleshow/37566815.cms http://world.time.com/2013/11/08/why-‐rape-‐seems-‐worse-‐in-‐india-‐than-‐everywhere-‐else-‐but-‐actually-‐isnt/ http://www.thehindu.com/news/national/a-‐freedom-‐parade-‐for-‐victims-‐of-‐sexual-‐violence/article4348773.ece http://www.thehindu.com/todays-‐paper/gangrape-‐main-‐accused-‐ram-‐singh-‐found-‐hanging/article4499112.ece http://www.thehindu.com/todays-‐paper/tp-‐national/mumbai-‐gang-‐rape-‐one-‐of-‐the-‐accused-‐is-‐a-‐minor-‐say-‐police/article5077686.ece http://www.thehindu.com/todays-‐paper/delhi-‐erupts-‐in-‐protest-‐over-‐rape/article4638734.ece

Social vs. Armchair Activism with Twitter

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