Challenge 16 Best Together addresses the issue of eliminating inequalities. In accordance with
the challenge we are facing together a systemic problem in the world's adolescent population.
We are proposing to close an existing social gap by encouraging the authority of adolescents in
a situation of self-injury. To prevent youth mortality from suicide using a combination of
humanity and technology is important in maintaining the generational balance of the future
population. We are integrating human care with artificial intelligence to meet the target
audience of our Splashlife project and predict its behavior. We want to eliminate self-provoked
violence through the use of listening. By using the application we hope that the adolescent will
achieve a behavior change from the experience he will have in our interface. We are a space
for channelling negative feelings. We want the adolescent to deposit in our system all the
energy he uses to practice self-injury. In Splashlife the user will learn about self care and
resilience to face his emotions and produce the best life he can live.
In 2017, during the III National Week of Science and Technology in Brazil, a 10-year-old girl
approaches the volunteer and asks if she can show her father in the Celeste Charter. The volunteer asks why. The girl answers that she wants to see her father die and she wants to find
him on the map. Surprised the volunteer talks to the girl and during the conversation she
notices scars on her arms. After a moment of conversation the girl tells the volunteer that she
always does that with her whenever she feels sad. Today this project is developed to bring
hope to girls and boys around the world. In Brazil in 2017, in the gender classification girls
represent 8018 (76%) cases and boys 2565 (24%). About the place of occurrence of self-injury
cases, the residence represents 41811 cases. In the classification by gender 29565 (71%) and
12246 (29%) (BRAZIL, 2017). The epidemiological profile indicates that these lesions
correspond to the highest risk factor for suicide (BRAZIL, 2017). Every 40 seconds a person dies
of suicide in the world. There are 6.5 deaths per 100,000 inhabitants (WHO, 2017). Our
challenge is to approach the issue from a prevention perspective. To create the application as
a protection factor we use tools available at the Space Apps Challenge website. The Discord
application was used for team interaction with mentors and facilitators. The Miro application
was used as a resource to find our solution. WhatsApp was used for instant messaging
between the team. After defining the solution we found the need to create a connection
between the project problem and the solution found. After receiving the teaching we used the
sentiment analysis tool that allows us to reach our target audience from the message tracking
and posted on social networks (NEURONIO. AI,2019). Natural Language Processing (NLP)
studies the interaction between artificial intelligence and human language code. The analysis
of feelings allows us to predict the emotion it has in a social network publication. With hashtgs
related to the project theme. To gain the confidence of our target audience we use targeted
advertising that allows a greater knowledge of the target audience and create a personalized
service focused on the needs of adolescents in a situation of self-injury (EGOI, 2018). From the
behavior that the teenager has already presented we want to create a personalized advertising
to invite her to know the app as a place of safety and protection.
In dates presented by CSA in 2016. In the Canada the suicide is a significante público health
problem. This resource indicated that is the nineth leading cause of death in country. About
adolescents with aged 10 to 29 is the second leading of death. Every day 10 people die by
suicide in Canada. This problem is complex 3.000.000 canadiang aged 15 above said they have
things suicide in your life. Approach the self-harm the hospitalizations among adolescents age
10-17 years old in periode of 2013-2014 notify to 1 in 4 cases of self-inflicted injury. To
registred for gender the girls with aged 10 t 17 years, the cases of self-harm are almost halfy
45% of all hospitalizations (CASA, 2016).
•CSA. World Suicide Prevention Day. Data blog. September 10, 2016. https://health-
infobase.canada.ca/datalab/suicide-blog.html
• NASA. Social impact of the Space Age. Why We Explore. Beyond Earth: Expanding Human
presence into the solar system. April, 2004.
https://www.nasa.gov/exploration/whyweexplore/Why_We_09.html
•NASA. NASA Unity Campaing. September, 2019.
https://www.nasa.gov/offices/odeo/nasaunity
•NASA. Impacto na Saúde mental. Space Apps Challenge, 2020. Discord. Oct 04, 2020.
•E-GOI. Publicidade direcionada: O que é e como aplicá-la no e-commerce? August 28, 2018.
https://blog.e-goi.com/br/publicidade-direcionada-o-que-e-aplicar-ecommerce/
•OHASHI, Rodrigo Nasaru. From Sentimental Analysis to Emotion Recognition: A NLP.
Neuronio.ai: Medium. July 24, 2019.
https://medium.com/neuronio/from-sentiment-analysis-to-emotion-recognition-a-nlp-story-
bcc9d6ff61ae
•BRASIL. Ministério da Saúde. Perfil Epidemiológico das tentativas e óbitos por suicídio no
Brasil e na rede de atenção à saúde. [Internet] 2017. [cited 2020 Oct 04]
•WHO. World Health Organization. Preventing suicide: a global interactive. [intertnet]. 2014.[cited 2020 Oct 04].
•PICCIN et al, 2019. The research out put onde child and adolescent suicide informações Brazil:
a systematic review of the literature. Braz J. Psychiatry. 2020 Mar- Apr. [Internet]. [Cited 2020
Oct 04].
NASA - Societal Impact of the Space Age
As controversies swirl about funding, resources, motives and methods for spaceflight, it is well
to consider the consequences of exploring space – and of choosing not to do so.
NASA
NASA Unity Campaign
NASA.gov brings you the latest images, videos and news from America's space agency. Get the
latest updates on NASA missions, watch NASA TV live, and learn about our quest to reveal the
unknown and benefit all humankind.
Medium
From Sentiment Analysis to Emotion Recognition: A NLP story
How we can use Machine Learning to recognize emotions from Social Media posts