2017 bullyblocker poster v5 final · dwic = total insults photo comment insults +1 feed insults +1...

Identification and Notification Process Account login and selection Bullying Risk (Bullying Rank and key measures) BullyBlocker aims to be a tool for parents’ involvement in their children's safety Designing and Implementing a Model to Identify Cyberbullying in Facebook Problem and Contributions • Cyberbullying is the use of online digital media to communicate false, embarrassing, or hostile information about another person. • It is the most common online risk for adolescents and well over half of young people do not tell their parents when it occurs. • There have been many studies about the nature and prevalence of cyberbullying. • Relatively less work in the area of automated identification of cyberbullying. • The focus of our work is to develop an automated model to identify and measure the degree of cyberbullying in social networking sites. • We present the design of a model for identifying cyberbullying that builds on previous research findings in the areas of traditional bullying and cyberbullying in adolescents. • We also identify challenges and opportunities to integrate the latest results from psychology and social network data analysis to address a problem of great social impact. Design • BullyBlocker analyzes adolescents’ interactions with their social network to identify: • Warning signs: e.g., number of insulting messages and embarrassing pictures. • States of vulnerability: e.g., newcomers, age-gender group, members of minority groups, people with disabilities, etc. • The integration of these factors is guided by previous research results in psychology. • Bullying Rank: Estimates the probability of a minor experiencing cyberbullying. • The computed Bullying Rank is returned to the parent or guardian of the minor. • The Bullying Rank Is divided into three normalized levels of risk intensity: Low[0-33], Medium[34-66], High[67-100] Risk Factors Architecture Advisor: Yasin N. Silva Students: Chance Brown | Liz Garcia | Bryan Sawkins Publications • Y. N. Silva, C. Rich, J. Chon, L. M. Tsosie. BullyBlocker: An App to Identify Cyberbullying in Facebook. The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016. • Y. N. Silva, C. Rich, D. Hall. BullyBlocker: Towards the Identification of Cyberbullying in Social Networking Sites. The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016. • L. M. Tsosie, Y. N. Silva. Facebully: Towards the Identification of Cyberbullying in Facebook. The Grace Hopper Celebration of Women in Computing (GHC), Minnesota, USA, 2013. Bullying Rank (BR) [0-100] BR = 90*WS + 10*VF Warning Signs (WS) [0-1] WS = (DWIC^1.6)/(DWIC^1.6+.05) Vulnerability Factors (VF) [0-1] VF = NSF*.33 + NNF*.33+AGF*.33 Age and Gender Factor (AGF) [0-1] New Neighborhood Factor (NNF) [0-1] NNF = 1 – ((DNN/NNDT)^4) New School Factor (NSF) [0-1] NSF = 1 – ((DNS/NSDT)^4) Days Since New School (DNS) New School Decay Time (NSDT) Days Since New Neighborhood (DNN) New Neighborhood Decay Time (NNDT) Daily Weighted Insult Count (DWIC) DWIC = Total Insults Photo Comment Insults +1 Feed Insults +1 BR levels Low risk: [0,33] Moderate risk: [34,66] Severe risk: [67,100] Data Collection Module Permanent Storage Cyberbullying Identification Module Bullying Rank Learning Module Bullying Rank Computation Notification (Bullying rank, stats, resources) Parent Feedback Facebook Graph API Child Facebook Account Parent Resource Generator Warning Signs Vs. Daily Insult Count New Neighborhood/School Factor Vs. Time Feedback and Anti- bullying Resources 1 2 3 4 5 6 7 8 9 Warning Signs(WS) Daily Weighted Insult Count

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Page 1: 2017 BullyBlocker Poster V5 Final · DWIC = Total Insults Photo Comment Insults +1 Feed Insults +1 BR levels Low risk: [0,33] Moderate risk: [34,66] Severe risk: [67,100] Data Collection

Identification and Notification Process

Accountloginandselection

BullyingRisk(BullyingRankandkeymeasures)

BullyBlockeraimstobeatoolforparents’involvementintheirchildren'ssafety

Designing and Implementing a Model to Identify Cyberbullying in Facebook

Problem and Contributions• Cyberbullying is the use of online digital media to

communicate false, embarrassing, or hostileinformation about another person.

• It is the most common online risk for adolescents andwell over half of young people do not tell theirparents when it occurs.

• There have been many studies about the nature andprevalence of cyberbullying.

• Relatively less work in the area of automatedidentification of cyberbullying.

• The focus of our work is to develop an automatedmodel to identify and measure the degree ofcyberbullying in social networking sites.

• We present the design of a model for identifyingcyberbullying that builds on previous researchfindings in the areas of traditional bullying andcyberbullying in adolescents.

• We also identify challenges and opportunities tointegrate the latest results from psychology andsocial network data analysis to address a problem ofgreat social impact.

Design• BullyBlocker analyzes adolescents’ interactions with

their social network to identify:• Warning signs: e.g., number of insulting messages

and embarrassing pictures.• States of vulnerability: e.g., newcomers, age-gender

group, members of minority groups, people withdisabilities, etc.

• The integration of these factors is guided by previousresearch results in psychology.

• Bullying Rank: Estimates the probability of a minorexperiencing cyberbullying.

• The computed Bullying Rank is returned to the parentor guardian of the minor.

• The Bullying Rank Is divided into three normalizedlevels of risk intensity: Low[0-33], Medium[34-66],High[67-100]

Risk Factors

Architecture

Advisor: Yasin N. Silva Students: Chance Brown | Liz Garcia | Bryan Sawkins

Publications• Y. N. Silva, C. Rich, J. Chon, L. M.

Tsosie. BullyBlocker: An App to IdentifyCyberbullying in Facebook. The 2016IEEE/ACM International Conference onAdvances in Social Networks Analysisand Mining (ASONAM), San Francisco,CA, USA, 2016.

• Y. N. Silva, C. Rich, D. Hall.BullyBlocker: Towards the Identificationof Cyberbullying in Social NetworkingSites. The 2016 IEEE/ACMInternational Conference on Advancesin Social Networks Analysis and Mining(ASONAM), San Francisco, CA, USA,2016.

• L. M. Tsosie, Y. N. Silva. Facebully:Towards the Identification ofCyberbullying in Facebook. The GraceHopper Celebration of Women inComputing (GHC), Minnesota, USA,2013.

BullyingRank(BR)[0-100]

BR=90*WS+10*VF

WarningSigns(WS)[0-1]

WS=(DWIC^1.6)/(DWIC^1.6+.05)

VulnerabilityFactors(VF)[0-1]

VF=NSF*.33+NNF*.33+AGF*.33

AgeandGenderFactor(AGF)[0-1]

NewNeighborhoodFactor(NNF)[0-1]

NNF=1–((DNN/NNDT)^4) NewSchoolFactor(NSF)[0-1]

NSF=1–((DNS/NSDT)^4)

DaysSinceNewSchool(DNS)

NewSchoolDecayTime(NSDT)