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#WhyIStayed #WhyILeft social media analysis

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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.

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#WhyIStayed #WhyILeft social media analysis

  1. 1. #WHYISTAYED ​ An analysis of #WHYISTAYED and #WHYILEFT data ​ Sept 8 – Dec 1 (inclusive), 2014 +
  2. 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. 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. 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. 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. 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. 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. 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. 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.