Nearly 200K posts and mentions covered the backlash on social media against the victim-blaming of Ray Rice's fiancee last September. Big Mountain Data was listening.

1. #WHYISTAYED
​ An analysis of #WHYISTAYED and #WHYILEFT data
​ Sept 8 – Dec 1 (inclusive), 2014
+

2. Background
Online conversations around the
terms, ‘WhyILeft’ and ‘WhyIStayed’
peaked in September 2014, when
people around the world began to
share their experiences with
domestic violence and abuse.
Individuals from both genders and
in married, unmarried, straight and
gay relationships, posted their
stories. This report analyzed those
conversations and the main
findings are included.

3. #WHYISTAYED Conversation Highlights
• The majority of mentions were made on Twitter, followed by Facebook and Mainstream News
• Over 99% of mentions used hashtags (#) and 689M Twitter users were reached
• Over 112k of the mentions were Retweets, while 85k were original posts
• Most mentions were written in English, while 78% were from individuals in the United States
• The most common reason people said they stayed with abusive partners was to keep their family
together
• The most reported reason why people left was fear for their children
• Over 75% of Twitter mentions were from non-celebrities (less than 1k followers), while Ashton Kutcher
was the celebrity with the highest reach
• Common hashtags used included #HowIHelped, #DomesticViolence, #DomesticAbuse,
#whenILeft and #DV
198,696
Posts on all Media
MENTIONS
TOP MEDIA TYPES
90% 6% 3%
3
*Some posts included both WHYISTAYED
and WHYILEFT
WHYISTAYED: 185,794
WHYILEFT: 63,883
BREAKDOWN

4. Why they Stayed
3
15,051
12,548
9,116
5,114 4,555 3,772 3,667
1,634 1,298 942 871
Keep Family
Together
Love Scared Financial Hope for
Change
No Support Fear being
Alone
Guilt Religion Didn't know it
was abuse
Low Self-
esteem
• The most common reason why individuals stayed with a
violent or abusive partner was to keep their family together –
women especially were afraid of being single mothers and felt
guilty when considering taking their partner away from their
children
• Love was the second most common reason people stayed,
followed by being threated, manipulated or scared to leave
their partner – in many of these cases, individuals reported
that they were fearful for their lives
• Other reasons for staying included fear of financial burden,
hope for the abusive partner to change, lack of a support
system, fear of being alone, guilt for leaving their partner,
strong religious views and low self-esteem
• Some individuals reported they did not think or believe that
the relationship or partner was abusive at the time
• Several people responded and replied to posts, and included
supportive comments and debates on what women should
have done in certain situations – a lot of empathy expressed

5. Everyday Voices
• The participation of regular people is one of the aspects of the conversation
that stands out the most. 133,972 of all Twitter posts came from people with
under 1,000 Twitter followers. Those people have been identified as non-
influencers. This makes up 75% of all Tweets. Only 11,307 of people who
used the hashtags had over 5,000 Twitter followers, a number typically
used to determine whether someone can be considered an influencer. This
made up for only 8% of all Tweets.
• It’s also interesting to note that non-influencers continued the conversation
longer than influencers did. By September 18 there were fewer than 100
daily mentions of the hashtags by influencers, however non0influencers
continued to provide over 100 mentions through most of October, reaching
that threshold occasionally in November.
• 133,972 people are enough to fill CenturyLink Field in Seattle to capacity
twice.
• Of the 133,972 Tweets, 66,174 were original Tweets (not Retweets). This
more than enough original voices to fill the Edward Jones Dome in St.
Louis.
The Edward Jones Dome, St. Louis

6. Celebrity Influencers
“Women Start #WhyIStayed Twitter Trend To Defend Those Who Stay In Abusive
Relationships http://aplus.com/a/why-i-stayed-domestic-violence-awareness …”
– Sept 10, 2014
Ashton Kutcher
@aplusk
17.1M
Followers
“Lots of discussion around @bevtgooden & #WhyIStayed. Leaving an abuser isn't easy, but
there is help: http://bit.ly/DV_Resources #endthesilence”
– Sept 18, 2014
Doctor Phil
@DrPhil
1.4M Followers
“When i was 9 i stopped my mom's boyfriend from stabbing my mom to death. if you mock
domestic abuse i have no time for you. #WhyIStayed”
– Sept 12, 2014
Moby
@thelittleidiot
1.3M
Followers

7. Non-Celebrity Influencers
“#WhyIStayed Because I didn't know the value of my own life and love.
#DomesticViolenceAwareness”
– Sept 21, 2014
Edie Summers
@ediesummers
1K Followers
“#WhyIStayed I didn't believe in Divorce. I loved my husband & wanted to make the
marriage work no matter what. If he broke a bone I'd leave.”
– Sept 12, 2014
Patty Felker
@noteworthytunes
1K Followers
“@JuddLegum @bevtgooden I remember the moment I saw this wheel-in DV training &
cried-I recognized for the 1st time I was a victim #WhyILeft”
– Sept 9, 2014
Urbanly DIVA
Inc.
@UrbanlyDIVA
1K Followers

8. Methodology
About Salesforce Radian6
Salesforce Radian6 provides social media monitoring, measurement, and engagement solutions. The Salesforce Radian6 platform was leveraged to acquire social media
data for this report. This platform scours nearly 550 million online conversations and stores approximately 180 million on-topic posts in our database daily. It also collects
social data from 500 million sources, adding approximately 5 million new sources each week. Our customers have access to a content archive of over 55 billion social
posts.
Keyword Setup
Search queries within Salesforce Radian6, also referred to as Topic Profiles, are constructed with keywords and utilize Boolean logic and filtering techniques (region, media
type, and language) to optimize results. Results in the platform are aggregated based upon an exact keyword system and as such, spelling variations of keywords are
included to capture natural misspells. The data can be further analyzed through segmentation by parameters such as date, language, region, sentiment, Twitter reach,
comment count, influence, number of posts, view count, likes and votes, on-topic inbound links, total inbound links, and unique source count.
Automated Sentiment
The Salesforce Radian6 automated sentiment algorithm utilizes natural language processing to assess sentiment at the keyword/phrase level. Sentiment is scored on a 6
point scale that includes: positive, somewhat positive, mixed, neutral, somewhat negative, and negative. Accuracy of the Salesforce Radian6 automated sentiment
algorithm is within accepted industry standards for automated sentiment analysis.

9. Methodology
Blogs
Online publications, usually consisting of opinion or commentary and often from a single author. All major blog hosting platforms are monitored, as well as self-hosted implementations.
Mainstream News
Online publications, usually consisting of impartial articles, often written by reporters and may also include republished information from wire services such as Reuters and Associated
Press.
Comments
Comments are short remarks left by site visitors in relation to posts of other media types. On sites where they are enabled, comments may be made on blog posts, news articles,
forum posts or replies, videos, images, and posts on social networks.
Twitter
The Marketing Cloud is one of the few companies to have access to the full Twitter Firehose. The full Twitter Firehose is the most complete coverage of Twitter, designed to pull in
publicly available Tweets in near real-time.
Facebook
Facebook data is limited to fan pages and publically available profiles and status updates; data is also determined by Facebook’s internally used algorithm “edge rank”, which assigns
value to posts based on various forms of engagement.
Forums and Forum Replies
Online communities, usually but not necessarily centred around a specific product or topic of interest, where members post and discuss new topics or “threads” organized by category.
The originating post in a thread is a “forum post” and all replies to it are classified as “forum replies”.
Images
Static images posted on image-sharing websites. Images must have a title and/or description to be keyword-matched.
Videos
Video clips posted on video-sharing websites. Videos must have a title and/or description to be keyword-matched.