Extracting interesting concepts from large-scale textual dataVasileios Lampos
Over the past few years large-scale textual resources, such as news articles, social media, search queries or even digitised books, have been the centre of various research efforts. In particular, the open nature of the microblogging platform of Twitter provided the unique opportunity for various appealing ideas to be evaluated. Based on the hypothesis that this online stream of content should represent at least a biased fraction of real-world situations or opinions, we have proposed core algorithms for estimating the current rate of an infectious disease, such as influenza, or even of a natural, less stable phenomenon like rainfall rates. A simplified emotion analysis on a longitudinal set of tweets revealed interesting patterns, including signs of rising anger and fear before the UK riots in August, 2011. A similar analysis on the Google N-gram book database uncovered collective patterns of affect over the course of the 20th century. Through the extension of linear text regression approaches by the addition of a user dimension, we proposed a family of bilinear regularised regression models, which found application in the approximation of voting intention trends. Finally, we reversed the previous modelling principle in an attempt to qualitatively analyse user attributes or behaviours.
Risk reduction on new drugs, a spanish experience (iván fornís)ivanoamapolo
ReDNet Conference.
First International Conference on Novel Psychoactive Substances (NPS):
"The Ever-Changing World of Psychoactive Drugs"
will be held on 12-13 March 2012
in Budapest, Hungary.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
We use metadata of various kind to improve and enrich text document clustering using an extension of Latent Dirichlet Allocation (LDA). The methods are fully implemented, evaluated and software is available on github.
These are the slides of an invited talk I gave September 8 at the Alexandria Workshop of TPDL-2016: http://alexandria-project.eu/events/3rd-workshop/
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Extracting interesting concepts from large-scale textual dataVasileios Lampos
Over the past few years large-scale textual resources, such as news articles, social media, search queries or even digitised books, have been the centre of various research efforts. In particular, the open nature of the microblogging platform of Twitter provided the unique opportunity for various appealing ideas to be evaluated. Based on the hypothesis that this online stream of content should represent at least a biased fraction of real-world situations or opinions, we have proposed core algorithms for estimating the current rate of an infectious disease, such as influenza, or even of a natural, less stable phenomenon like rainfall rates. A simplified emotion analysis on a longitudinal set of tweets revealed interesting patterns, including signs of rising anger and fear before the UK riots in August, 2011. A similar analysis on the Google N-gram book database uncovered collective patterns of affect over the course of the 20th century. Through the extension of linear text regression approaches by the addition of a user dimension, we proposed a family of bilinear regularised regression models, which found application in the approximation of voting intention trends. Finally, we reversed the previous modelling principle in an attempt to qualitatively analyse user attributes or behaviours.
Risk reduction on new drugs, a spanish experience (iván fornís)ivanoamapolo
ReDNet Conference.
First International Conference on Novel Psychoactive Substances (NPS):
"The Ever-Changing World of Psychoactive Drugs"
will be held on 12-13 March 2012
in Budapest, Hungary.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
We use metadata of various kind to improve and enrich text document clustering using an extension of Latent Dirichlet Allocation (LDA). The methods are fully implemented, evaluated and software is available on github.
These are the slides of an invited talk I gave September 8 at the Alexandria Workshop of TPDL-2016: http://alexandria-project.eu/events/3rd-workshop/
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Pavan Kapanipathi's talk at IBM's Frontiers of Cloud Computing and Big Data Workshop 2014. http://researcher.ibm.com/researcher/view_group_subpage.php?id=5565
Due to the increased adoption of social web, users, specifically Twitter users are facing information overload. Unless a user is willing to restrict the sources (eg number of followings), important information relevant to users' interests often go unnoticed. The reasons include (1) the postings may be at a time the user is not looking for; (2) the user unaware and hence not following the information source; (3) and the information arrives at a rate at which the user cannot consume. Furthermore, some information that are temporally relevant, discovered late might be of no use.
My research addresses these challenges by
(1) Generating user profiles of interests from Twitter using Wikipedia. The interests gleaned from users' Twitter data can be leveraged by personalization and recommendation systems in order to reduce information overload/Volume for users.
(2) Filtering twitter data relevant to dynamically evolving entities. Including Volume, this addresses the velocity challenge in delivering relevant information in real-time. The approach is deployed on Twitris to crawl for dynamic event-relevant tweets for analysis. The prominent aspect of the approaches is the use of crowd-sourced knowledge-base such as Wikipedia.
What do you do with 280 million tweets from the 2016 U.S. election?Justin Littman
A talk at Social Media Lab, Ryerson University on April 25, 2018 discussing the 2016 U.S. election Twitter dataset collected at George Washington University Libraries.
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Cyber Pun...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Team Cyber Punk, Charotar University (Prince Makwana, Pritul Dave, Kushal Master)
AI in the Social Sciences Presentation April Heyward
April Heyward gave an invited talk titled "AI in the Social Sciences" for the Hawaii Data Science Institute on how she employs Artificial Intelligence in her research.
Interested in evidence-based criteria for clear communication of public health material?
Do you develop and review public health material? Are you interested in enhancing the clarity and ease of understanding of these materials? The Clear Communication Index can help!
The U.S. Centers for Disease Control and Prevention developed the Index.
How can the Clear Communication Index help you?
The Index helps professionals develop and review public health communication materials for clarity, encourages collaboration between writers and reviewers to ensure accuracy, and assesses ease of use of communication materials. The tool consists of criteria that enhance clarity and understanding.
Use of the Index yields a quantitative score based on assessment of seven areas: main message and call to action, language, information design, state of the science, behavioural recommendations, numbers, and risk. A widget is available for placement on webpages.
This webinar includes an overview of the Index by its developers, followed by a presentation from the National Resource Center for Lupus in the Lupus Foundation of America.
To see the summary statement of this method developed by NCCMT, click here: http://www.nccmt.ca/resources/search/247
The National Collaborating Centre for Methods and Tools is funded by the Public Health Agency of Canada and affiliated with McMaster University. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.
NCCMT is one of six National Collaborating Centres (NCCs) for Public Health. The Centres promote and improve the use of scientific research and other knowledge to strengthen public health practices and policies in Canada.
We believe open annotation is a unique new capability that has the potential to radically transform the way we engage with scientific content across the web. Not only annotations are central in the realization of the web of documents by facilitating the formalization and discovery of relations across papers. Most importantly, annotations, we argue, are central in the empowerment of communities of practices by making content based conversations possible. In addition, the activity arising from such content based exploration allows for the definition of a novel alternative metric. As the annotation is specific to a part of the text, it allows for granular analysis of the paper; such metric tells us not just the number of tweets or LIKEs for a given document. It also allows us to identify the topics that are arising interest and how are these being discussed. The contribution is therefore twofold; on the one hand NanoTweets are a type of community based annotation, on the other, hand, NanoTweets are also delivering a granular metric rooted within the content of the document -simplifying content based business intelligence.
Advancing Team-Based Care:Data Driven Dashboards to Support Team Based Care CHC Connecticut
This webinar highlighted the ways that practices utilize technology to improve individual patient care and track and meet the needs of their whole patient population. By using electronic health record data and clinical dashboards, members of the team can organize visits to resolve care gaps, optimize prevention, and improve clinical outcomes.
This webinar was presented April 7, 2016 3:00 PM Eastern Time
In search of a Digital Health CompassPatient Empowerment chronaki
Presentation of the digital health compass in the Portuguese eHealth Summer Week with Anne Moen (U of Oslo), Catherine Chronaki (HL7), Rita Mendes (SPMS). Great moderation by Constantino Sakellarides, ENSP.
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Weitere ähnliche Inhalte
Ähnlich wie eDrugTrends: Social Media Analysis to Monitor Cannabis Trends
Pavan Kapanipathi's talk at IBM's Frontiers of Cloud Computing and Big Data Workshop 2014. http://researcher.ibm.com/researcher/view_group_subpage.php?id=5565
Due to the increased adoption of social web, users, specifically Twitter users are facing information overload. Unless a user is willing to restrict the sources (eg number of followings), important information relevant to users' interests often go unnoticed. The reasons include (1) the postings may be at a time the user is not looking for; (2) the user unaware and hence not following the information source; (3) and the information arrives at a rate at which the user cannot consume. Furthermore, some information that are temporally relevant, discovered late might be of no use.
My research addresses these challenges by
(1) Generating user profiles of interests from Twitter using Wikipedia. The interests gleaned from users' Twitter data can be leveraged by personalization and recommendation systems in order to reduce information overload/Volume for users.
(2) Filtering twitter data relevant to dynamically evolving entities. Including Volume, this addresses the velocity challenge in delivering relevant information in real-time. The approach is deployed on Twitris to crawl for dynamic event-relevant tweets for analysis. The prominent aspect of the approaches is the use of crowd-sourced knowledge-base such as Wikipedia.
What do you do with 280 million tweets from the 2016 U.S. election?Justin Littman
A talk at Social Media Lab, Ryerson University on April 25, 2018 discussing the 2016 U.S. election Twitter dataset collected at George Washington University Libraries.
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Cyber Pun...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Team Cyber Punk, Charotar University (Prince Makwana, Pritul Dave, Kushal Master)
AI in the Social Sciences Presentation April Heyward
April Heyward gave an invited talk titled "AI in the Social Sciences" for the Hawaii Data Science Institute on how she employs Artificial Intelligence in her research.
Interested in evidence-based criteria for clear communication of public health material?
Do you develop and review public health material? Are you interested in enhancing the clarity and ease of understanding of these materials? The Clear Communication Index can help!
The U.S. Centers for Disease Control and Prevention developed the Index.
How can the Clear Communication Index help you?
The Index helps professionals develop and review public health communication materials for clarity, encourages collaboration between writers and reviewers to ensure accuracy, and assesses ease of use of communication materials. The tool consists of criteria that enhance clarity and understanding.
Use of the Index yields a quantitative score based on assessment of seven areas: main message and call to action, language, information design, state of the science, behavioural recommendations, numbers, and risk. A widget is available for placement on webpages.
This webinar includes an overview of the Index by its developers, followed by a presentation from the National Resource Center for Lupus in the Lupus Foundation of America.
To see the summary statement of this method developed by NCCMT, click here: http://www.nccmt.ca/resources/search/247
The National Collaborating Centre for Methods and Tools is funded by the Public Health Agency of Canada and affiliated with McMaster University. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.
NCCMT is one of six National Collaborating Centres (NCCs) for Public Health. The Centres promote and improve the use of scientific research and other knowledge to strengthen public health practices and policies in Canada.
We believe open annotation is a unique new capability that has the potential to radically transform the way we engage with scientific content across the web. Not only annotations are central in the realization of the web of documents by facilitating the formalization and discovery of relations across papers. Most importantly, annotations, we argue, are central in the empowerment of communities of practices by making content based conversations possible. In addition, the activity arising from such content based exploration allows for the definition of a novel alternative metric. As the annotation is specific to a part of the text, it allows for granular analysis of the paper; such metric tells us not just the number of tweets or LIKEs for a given document. It also allows us to identify the topics that are arising interest and how are these being discussed. The contribution is therefore twofold; on the one hand NanoTweets are a type of community based annotation, on the other, hand, NanoTweets are also delivering a granular metric rooted within the content of the document -simplifying content based business intelligence.
Advancing Team-Based Care:Data Driven Dashboards to Support Team Based Care CHC Connecticut
This webinar highlighted the ways that practices utilize technology to improve individual patient care and track and meet the needs of their whole patient population. By using electronic health record data and clinical dashboards, members of the team can organize visits to resolve care gaps, optimize prevention, and improve clinical outcomes.
This webinar was presented April 7, 2016 3:00 PM Eastern Time
In search of a Digital Health CompassPatient Empowerment chronaki
Presentation of the digital health compass in the Portuguese eHealth Summer Week with Anne Moen (U of Oslo), Catherine Chronaki (HL7), Rita Mendes (SPMS). Great moderation by Constantino Sakellarides, ENSP.
Ähnlich wie eDrugTrends: Social Media Analysis to Monitor Cannabis Trends (20)
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
2. Research Team
NIH/NIDA R01 DA03945
Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use
Principle Investigators:
Raminta Daniulaityte, Ph.D. Amit Sheth, Ph.D.
Center for Interventions, Treatment, and
Addiction Research (CITAR),
Wright State University
Boonshoft School of Medicine
Ohio Center of Excellence in Knowledge-
Enabled Computing (Kno.e.sis),
Wright State University
Co-Investigators:
Robert Carlson, Ph.D. (CITAR) Silvia Martins, M.D., Ph.D. (Columbia U)
Ramzi Nahhas, Ph.D. (Comm. Health, WSU) Edward Boyer, M.D., Ph.D. (U Mass)
Krishnaprasad Thirunarayan, Ph.D. (Kno.e.sis)
Research Staff:
Francois R. Lamy, PhD (CITAR, Postdoc);
G. Alan Smith (Kno.e.sis, Software Engineer);
Sanjaya Wijeratne (Kno.e.sis, Ph.D. student)
Farahnaz Golroo (Kno.e.sis, Ph.D. student)
No Conflicts of Interest to declare
3. Project Aims
• Aim 1: Develop a comprehensive software platform, eDrugTrends, for
semi-automated processing and visualization of spatio-temporal, and social
network dimensions of social media data (Twitter and Web forums) on
cannabis and synthetic cannabinoid use.
• Aim 2: Deploy eDrugTrends to identify and compare trends in knowledge,
attitudes, and behaviors related to cannabis and synthetic cannabinoid use
across U.S. regions with different cannabis legalization policies using Twitter
and Web forum data.
• Types of data sources:
o Twitter (brief content, but over 500 million tweets/day, geo-info)
o Web forums such as Bluelight, drugs-forum, Reddit (detailed discussions of drug
use practices)
o Web survey on Bluelight
4. Presentation Objectives
• Overview of the technical capabilities of eDrugTrends platform
to process Twitter data
• How data is collected
• Geo-location identification
• Keyword selection and monitoring
• Tweet content processing
• Exploration of recently collected and processed data on
marijuana concentrates
• Integrating geographic and content analysis features to
explore cannabis-related tweeting activity
5. :
Twitter Data Collection
• Tweets are collected using Twitter’s streaming Application Programming
Interface (API) that provides free access to 1% of all tweets.
• Publically available tweets only.
• The system automatically filters out non-English language tweets.
• Current system started data collection March 2015; Close to 90 million
tweets have been collected
eDrugTrends Dashboard Showing in-coming Tweets and trending Topics
6. What does “up to 1%” mean?
• Free access to 1% of all tweets
o It can be thought of as a ”bucket” that can fit up to 1% of all tweets.
o Assuming 400 million daily tweets are generated per day, 1% would constitute
about 4 million daily tweets.
o Still, it is possible to miss some of the tweets due to sudden volume spikes.
• With a reasonably limited number of keywords, all or
most relevant tweets can be collected.
• Our system collects an average of about 150,000 tweets
per day, which is below the allowable limit.
7. Extraction of Geo-Location Information
• Tweets may contain GPS coordinates (via a mobile phone
that supports the feature).
• Users may indicate their geo information in their user profiles:
WHERE THE WEED AT
DAYTON, OH
SAN DIEGO
Pittsburgh, PA
wonderland
Earth
• eDrugTrends geo-locates close to 30% of tweets for state-
level and county-level information .
• Some earlier studies reported 1-3%of tweets with geo-location identification.
8. Adjusted Measures of Tweeting Activity
• To compare regional trends, we can’t work with raw numbers.
• eDrugTrends started running a parallel data collection system
to obtain general sample of tweets (denominator data).
• General sample data are collected using another API stream;
no keywords are used; data are processed to identify
geographic information.
• “General sample” is then used to calculate state-tweet-
volume-adjusted state proportion of tweets
o (or county-tweet-volume-adjusted county proportion of tweets)
9. RAW Numbers and ADJUSTED State Proportions of
Cannabis-Related Tweets
(March-September, 2016)
Raw numbers
Adjusted proportions
10. Twitter Data Collection: Keywords
• Keywords/slang terms are used to collect relevant tweets:
o Cannabis—weed, marijuana, spliff, ganja, kush, sativa, indica, chronic, blunt,
hydro, skunk, reefer, joint, etc.
o Marijuana concentrates—dabs, shatter, budder, wax BHO, butane honey oil,
hash oil, etc.
o Edibles—weed cookies, space cake, pot cookie, pot brownie, mj brownie,
medibles, etc.
o Synthetic cannabinoids—spice, K2, CHMINACA, AB-FUBINACA, synthetic weed,
smoking blend, noid, black mamba, etc.
• Inclusion of slang terms improves sensitivity (recall) in data
collection
11. Keyword challenges
• Issues with “precision”– risk of getting “noisy” or “irrelevant” data.
• Ways to improve precision of collected data:
o Ambiguous terms are combined with additional keywords indicating usage (e.g., smoke
blunt, smoke budder)
o “Black list” words are used to exclude irrelevant tweets (e.g., pumpkin spice latte, Emily
Blunt).
o Machine learning and other advanced information processing techniques are needed
• On-going monitoring is needed:
o New types of products or slang terms emerge. For example, “rosin”—new type of marijuana
concentrate produced using solvent-less method.
o New uses/meanings of words may affect the accuracy of collected data. (e.g., “dabs”)
12. Data Processing: Automated Tweets Classification
• Using manually annotated training
data sets, machine learning
classifiers were developed to
automatically classify tweets
• Classification by the the source/type
of communication (personal, media,
retail)
o Machine learning classifier (SVM)
achieved F score = 0.81.
• Classification by sentiment
(positive, negative, neutral),
o Sentiment classification is applied
to personal communications only
o Machine learning classifier (SVM)
achieved F score = 0.71.
Kickin back wit my spliff
Late night dabs
Medical marvel: the uses of cannabis
continue to grow
http://t.co/djtKPunxW9
$10 #Cannabis #Edibles 12 Varieties 1
Package 10MG #THC total
http://t.co/9w3xrFUnAe
Positive:
Marijuana works wonders on the soul
Strongest shatter I've ever smoked
Negative:
I’m not much of a fan when it comes to
edibles
hate when people think i smoke weed
14. Initial report about marijuana concentrate
related tweeting: “Time for dabs”
2014 data
• Data collected over 2 month period, end of 2014.
• 27,018 tweets with identifiable state-level geo-location
• Although over 10 keywords were used (shatter, concentrates, butane
hash oil, etc.), keyword “dabs” produced over 99% of the total sample.
Dabs on Dabs on Dabs
Time for dabs
I just need a cute girl to take
dabs with me and get stoned
together
Time for dabs": Analyzing Twitter data on marijuana concentrates across the U.S.
Daniulaityte R., Nahhas R.W., Wijeratne S., Carlson R.G., Lamy F.R., Martins S.S., Boyer E.W., (...), Sheth A.
(2015) Drug and Alcohol Dependence, 155 , pp. 307-311.
15. 2015: Increases in Marijuana Concentrate-Related
Tweeting Activity? Oops! Not So Fast…
0
2000
4000
6000
8000
10000
12000
14000
Jun
8th
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15th
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27th
Aug
3rd
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2nd
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Marijuana Concentrates US Tweeting Activity Jun-Nov, 2015
Tweets Unique users
16. Issues with Collected Data
Drug vs. Dance
Cam Newton cheers on Kevin Hart in a bench press challenge…then Dabs
Tell me why my mom DABS so well? https://t.co/7LZjdqBkQr
Cam celebrates, Cam dabs, Cam does Cam thing
17. Development of Machine Learning Classifier to
Extract Relevant Tweets
• Machine learning (ML) classifier was developed using 1,000 manually
labeled tweets
• Excellent results:
• ML classifier (NB) achieved F Measure=0.9; Kappa Statistic=0.8
• Dabs ML classifier was plugged into the system;
18. End of 2014
Start of 2017
• Similar geographic
patterns remained
• 96% were personal
communication tweets
(2017 data)
• Decrease in variability
across states:
Marijuana Concentrate Related Tweeting Over Time
19. Emerging Product: Rosin Tech
• Rosin technique is a solventless method to produce marijuana
concentrates
• Involves use of pressure and heat (e.g., hair straightener or rosin
tech press) to produce concentrates
• Occurrences of ‘rosin’ mentions in eDrugTrends steam (03 2015-09
2016), before “rosin” keyword was added
20. Rosin dabs: Preliminary data
• Keyword “Rosin” (exclude violin, brass, bow); Time period: December 6
2016- February 22 2017; 3,471 tweets collected (with identifiable state-level
geo-location)
YOOOO JUST PRESSED FOR THE FIRST TIME AND IT WAS LIFE
CHANGING 🙏🙏🙏🙏🔥😩 flower rosin is the new fav
The future is bright for #Rosin. #Marijuana #Cannabis
Nice chunk of rosin to start this morning off
2017 goal....buy a house & rosin press.
Marijuana rosin, and increasingly common extract:
https://t.co/tXZNErOPta
Rosin Tech Hash Is perfect for the people in non medical marijuana
states where it's hard to come across quality BHO to dab.
21. Adjusted Proportions of Rosin-Related Tweets
(Preliminary data, Dec. 6, 2016-Feb. 22, 2017)
84% - personal communication tweets
8% - media related
8% - retail related
Great Variability:
Mean: 1.96; Variance: 2.5
23. Cannabis Data, March–May, 2016
• Between March and May of 2016, the eDrugTrends
platform collected 13,233,837 cannabis-related tweets.
• About 30% (N=3,948,402) of those tweets had
identifiable state-level geo-location information.
• These U.S.-based tweets were posted by 965, 610
unique users.
24. Content Classification and Analysis
• Tweet content was automatically classified by:
A. source (personal communication, media, retail)
B. sentiment (positive, negative, neutral).
• States were grouped by cannabis legalization polices into
“recreational,” “medical, less restrictive,” “medical, more
restrictive,” and “illegal.”
• Permutation tests were performed to analyze differences
among four groups in:
A. Adjusted state proportions of all tweets,
B. personal communications only,
C. positive to negative sentiment ratios.
26. Adjusted state proportions of cannabis
related tweets
Adjusted tweet rate
per state
>3.0%
2.5%-3.0%
2.0%-2.49%
1.5%-1.9%
1.0%-1.49%
Medical Marijuana Legal
Recreational Marijuana Legal
27. Tweet Content Classification Results
Source/Type of communication
• 76.2% were personal communications,
• 21.1% media
• 2.7% retail-related
Sentiment
• About 71% of personal communication tweets expressed
positive sentiment towards cannabis,
• 16% negative sentiment,
• 13% were neutral.
30. Conclusion
• Social media data present exciting new opportunities
for timely, sensitive and flexible approaches to
epidemiological surveillance of drug use practices
and trends.
• Continued research is needed to establish
methodological standards and practices to reduce
the “noise” and increase reliability and validity of
social media data.
• Social media monitoring can be of particular value
for tracking cannabis-related trends in the context of
rapid policy changes.
31. Keep up with our research/publications:
@ project page:
http://wiki.knoesis.org/index.php/EDrugTrends
or Google: eDrugTrends
or Twitter: @eDrugTrends
Thank you!
Center for Interventions,
Treatment, and Addiction
Research (CITAR)
https://medicine.wright.edu/citar
Ohio Center of Excellence in
Knowledge-enabled
Computing (Kno.e.sis)
http://knoesis.org
Sponsored by:
Grant No. 5R01DA039454-02
Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use.
Any opinions, findings, conclusions or recommendations expressed in this material are those of the investigator(s)
and do not necessarily reflect the views of the National Institutes of Health.