Twitris is a technology developed at Kno.e.sis that provides real-time, actionable insights from social media data. It analyzes data through approaches like Sentiment-Emotion-Intent, Spatio-Temporal-Thematic, and People-Content-Network. Twitris has been applied in domains like disaster response, elections, public health, and social movements. It has been used to help coordinate responses during crises like hurricanes, floods, and tornadoes.

1. Applications of Twitris Technology: Real-time, Actionable
Insights from Social Media
Rotary Meeting, Fairborn, Ohio.16th May 2018
Prof. Amit Sheth
LexisNexis Ohio Eminent Scholar
Exec. Dir. - Kno.e.sis @ Wright State University

2. Data used for this simulation was based on repurposing of 2013 Boston Bombing dataset.
More: Hampton et al. Constructing Synthetic Social Media Stimuli for an Emergency Preparedness Functional Exercise. ISCRAM-2017.
Designing Real-time Coordination Tools:
Dayton Regional EM Exercise
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Snapshot
Twitris-based
simulation tool for
filtering social system
Used in a
functional exercise of
emergency response
organizations
City: Dayton
Date: 5/28/14

3. https://upload.wikimedia.org/wikipedia/commons/7/7c/Conversationprism.jpeg
Never before humanity is so connected
IEEE Internet Computing, 2009
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4. https://www.flickr.com/photos/25031050@N06/3292307605
ICWSM-13 Tutorial: Crisis Mapping & Citizen Sensing 4

5. The Origin
The idea for Twitris came on 26 Nov 2008 when terrorist
stuck at 9 locations in Mumbai for 3 days. It was clear that
social media had become a preferred way for sharing
breaking news and real-time news dissemination, but it was
hard to achieve situal awareness from the torrent of social
media posts.
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6. Social Data is incredibly rich.
Real-time analysis of
v1: Spatio-Temporal-Thematic
v2: People-Content-Network
v3: Sentiment-Emotion-Intention
v4: Semantic filtering/knowledge
graph, IFTTT, scalability, robustness
Commercial: Cognovi Labs
TWITRIS’ technical Approach to
Understand & Analyze Social Content
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Twitris Technology:
Real-time, Actionable Insights from Social-media
S-E-I
Sentiment-Emotion-Intent
Extracts and assigns structured
sentiment and emotion scoring
from unstructured content to
understand motivation,
feelings, opinion and intent.
S-T-T
Spatio-Temporal-Thematic
Provides thematic context
through analysis of place
and time.
P-C-N
People-Content-Network
Analyzes influential users
and identifies who is being
listened to.
Key Differentiators:
● Comprehensive (above)
● Semantic Processing: use of public and proprietary knowledge.
● Real-time processing: used in live blogging of election debate; coordination during
disasters.
● Scalable: deployed on a large cloud (864 CPUs, 17 TB main, 435 TB disk).

8. Snapshot of Some Real-world Applications/Trials
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9. Snapshot of Some Real-world Applications/Trials
Domains: Branding, Disaster Coordination, Social Movements, Election, Development, Epidemiology, etc.
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10. • Coordination during disasters (QCRI, Microsoft Research NYC, CrisisNET, UN)
• Harassment on social media (WSU cognitive scientists)
• Prescription drug and opioid abuse, Cannabis & Synthetic Cannabinoid
epidemiology (Center for Interventions, Treatment and Addictions Research, ….)
• Depressive disorders (Weill Cornell Med)
• Gender-based violence (UNFPA), Zika Spread
• and extensive applications in personalized digital health, public health
(Dayton Children’s Hospital, Wright St Physicians, …)
Highly multidisciplinary team efforts, often with significant partners, with real
world data, intended to achieve real-world impact
Some of the significant human, social & economic
development applications we work on at Kno.e.sis
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Public Interest: US Healthcare Reform Debate - 2009
More: https://www.youtube.com/watch?v=OHuFtHXlhtw

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Election Prediction: US Election - Nov., 2012
More: http://twitris.knoesis.org/insights/Election2012
http://www.dataversity.net/election-2012-the-semantic-recap/

13. Disaster Relief: Sample of Real-World Impact & Media
Coverage
Oct 12, 2013
Google's Person Finder and
Google Crisis Response Map
for Phailin to help with crisis
information
Jun 27, 2013
Using crisis mapping to
aid Uttarakhand
Jun 24, 2013
Twitris: Taking Crisis
Mapping to the Next Level
Sep 11, 2012
Could Twitris+ Be Used
for Disaster Response?
Dec 7, 2015
Chennai floods: How social
media and crowdsourcing
helps people on ground
Sep 9, 2014
Digital soldiers emerge
heroes in Kashmir flood
rescue
And many other topics: Emoji, Religion, Gun Violence, Public Policy, Smart City, Health, Election
(currently predicting: Election2012, Brexit, Election2016, ALSenate): http://knoesis.org/amit/media/

14. Example of coordination during #Oklahoma-tornado response based on
automatically matching need-offer pairs of community members.
More: Purohit et al. Emergency-relief coordination on social media: Automatically matching resource requests and offers. First Monday, 14
Disaster Relief: Oklahoma Tornado - May, 2013

15. Google Crisis Map for Hurricane Phalin, which used data from international participants spearheaded by
Twitris team at the Kno.e.sis center. More on - https://goo.gl/iB2LHg 15
Disaster Relief: Hurricane Phalin - Oct., 2013

16. More: Purohit et al. Empowering Crisis Response-Led Citizen Communities: Lessons Learned from JKFloodRelief.org Initiative. IGI Global
book, 2016.
Rescue and Evacuation Stream Map during the historic Jammu & Kashmir Floods in Sep/2014.
Twitris supported the scalable relief effort of JKFloodRelief.org initiative.
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Disaster Relief: Kashmir Floods - Sep., 2014

17. Moriam Nessa
Sep 8th, 11:09am
I do not know if anyone will be reading this
message and if this will be of any help. But I have
my sister who is stranded in Srinagar. She is 9
months pregnant and they need help. We have
been trying to get through the help lines but nothing
is working. Somebody please help ...
ADGPI - Indian Army
Sep 8th, 3:24pm
Jawahar Nagar is heavily flooded. Rescue teams
will be going there. So don't worry they will be
alright. Indian Army is there
Moriam Nessa
Sep 8th, 7:28pm
Hey, Thank you so much for your effort in rescue
operations. But I’m writing to you again about
update of the situation in Jawahar Nagar. My
concern is that a young girl there is pregnant.
Moriam Nessa
Sep 9th, 8:27am
Thank you so much again for your relief work. My
sister has been rescued. All thanks to you and your
team.
17More: https://www.hindustantimes.com/india/digital-soldiers-emerge-heroes-in-kashmir-flood-rescue/story-WuHYPkoQxNtJQzvVMLDnqK.html
Disaster Relief: Kashmir Floods - Sep., 2014

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Other: U.S. Religious Landscape - Oct., 2014
More: https://www.technologyreview.com/s/531446/twitter-data-mining-reveals-americas-religious-fault-lines/
Lu Chen et al., U.S. Religious Landscape on Twitter. Social Informatics (SocInfo), 2014.

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Other: City Traffic Event Identification - Jul., 2015
More: Pramod Anantharam et al.,. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43
(July 2015)

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Social Good: Combating Cyberbullying - Sep., 2015
More: https://youtu.be/gL1e0dQre_0
http://wiki.knoesis.org/index.php/Context-Aware_Harassment_Detection_on_Social_Media

21. Twitris Chennai Flood Map
21More: https://www.oneindia.com/india/chennai-floods-rescue-operations-social-media-technology-twitter-1947228.html
Disaster Relief: Chennai Floods - Dec., 2015

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Social Good: Gender-based Violence - Jan., 2016
More: Hemant Purohit et al., Gender-Based Violence in 140 Characters or Fewer: A #BigData Case Study of Twitter, First Monday, 2016.

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Public Referendum: Brexit - Jun., 2016
More: https://techcrunch.com/2016/06/29/the-twitris-sentiment-analysis-tool-by-cognovi-labs-predicted-the-brexit-hours-earlier-than-polls/

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Election Prediction: US Election - Nov., 2016
More: https://www.whio.com/news/local/wsu-researchers-monitored-twitter-predict-trump-victory/pdPJiwLFxNKCH5J7zER9BJ/
https://www.linkedin.com/pulse/election-day-socialmedia-analysis-election2016-06nov2016-amit-sheth/

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Social Good: Street Gang Member Profile Identification -
Nov., 2016
More: http://webapp2.wright.edu/web1/newsroom/2018/03/06/gang-guidance/
Lakshika Balasuriya et al., Finding Street Gang Members on Twitter. IEEE/ACM ASONAM, 2016

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Health: Understanding Edible Marijuana Use
in the USA - Apr., 2017
More: http://www.daytoncitypaper.com/eat-it-then-tweet-it/
Lamy, Francois R. et al., “Those edibles hit hard”: Exploration of Twitter data on cannabis edibles in the U.S, Drug & Alcohol Dependence ,
Volume 164 , 64 - 70

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Health: Combatting Depression - Jul., 2017
More: http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression
Amir Yazdavar et al., Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media. IEEE/ACM ASONAM, 2017

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Election Prediction: Alabama Senate - Dec., 2017
More: http://www.centerforpolitics.org/crystalball/articles/can-twitter-predict-elections/
https://www.linkedin.com/pulse/hat-trick-aka-triple-play-going-against-tide-calling-election-sheth/

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Other: Emoji Understanding - Jan., 2018
More: Sanjaya Wijeratne et al., EmojiNet: An Open Service and API for Emoji Sense Discovery. ICWSM 2017. Montreal, Canada; 2017.
http://emojinet.knoesis.org/home.php

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31. More details treatment of some of the technical topics
• http://wiki.knoesis.org/index.php/Twitris, http://twitris.knoesis.org
• WWW2011: “Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications,”
with Meena Nagarajan and Selvam Velmurugan.
• ICWSM2013:”Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis
Response,” with Carlos Castillo, Patrick Meier, and Hemant Purohit.
Media stories: http://knoesis.org/amit/media
Twitris and/or Semantic Social Web Research @ Kno.e.sis: : K. Gomadam, M. Nagarajan, A.
Ranabahu, H. Purohit, W. Wang, L. Chen, P. Anantharam, A. Jadhav, P. Kapanipathi, Alan Gary
Smith, Jeremy Brunn, Mike Partin, Dr. T.K. Prasad, Dr. Valerie Shalin and many others
Funding: NSF, AFRL, NIH; Collaborations: IBM, Microsoft
Special thanks (preparing this presentation): Sanjaya Wijeratne
Acknowledgements
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32. Thank You!