This research aims to characterize HIV at-risk populations among men who have sex with men (MSM) in San Diego by analyzing social media data. The researchers collect tweets from San Diego and classify them based on risk categories like drug use, sex venues, etc. They build a social network graph of Twitter users and their connections and compare the structure to the real-world HIV transmission network. Exploratory analysis of the social graph reveals patterns in topics of discussion and network structures that can help predict HIV transmission risk and enable prevention efforts. Future work includes further data collection, interactive visualizations, and computational models to understand how the social network evolves and relates to the sexual network transmitting HIV.
Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date.
Poster presentation in 3rd big data conclave at vit chennai on 20th april 2017Rohit Desai
Title:Leveraging Big Data and Social Sensors For Predicting Epidemic Disease Outbreak
Event & Venue:3rd Big Data Conclave at VIT Chennai on 20th & 21st April 2017
Project Conducted Under Guide:Dr.Sweetlin Hemalatha Professor at VIT Chennai
A network based model for predicting a hashtag break out in twitter Sultan Alzahrani
Online information propagates differently on the web, some
of which can be viral. In this paper, first we introduce a simple standard deviation sigma levels based Tweet volume breakout definition, then we proceed to determine patterns of re-tweet network measures to predict whether a hashtag volume will breakout or not. We also developed a visualization tool to help trace the evolution of hashtag volumes, their underlying networks and both local and global network measures. We trained a random forest tree classifier to identify effective network measures for predicting hashtag volume breakouts. Our experiments showed that “local” network features, based on a fixed-sized sliding window, have an overall predictive accuracy of 76 %, where as, when we incorporate “global” features that utilize all interactions up to the current period, then the overall predictive accuracy of a sliding window based breakout predictor jumps to 83 %.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date.
Poster presentation in 3rd big data conclave at vit chennai on 20th april 2017Rohit Desai
Title:Leveraging Big Data and Social Sensors For Predicting Epidemic Disease Outbreak
Event & Venue:3rd Big Data Conclave at VIT Chennai on 20th & 21st April 2017
Project Conducted Under Guide:Dr.Sweetlin Hemalatha Professor at VIT Chennai
A network based model for predicting a hashtag break out in twitter Sultan Alzahrani
Online information propagates differently on the web, some
of which can be viral. In this paper, first we introduce a simple standard deviation sigma levels based Tweet volume breakout definition, then we proceed to determine patterns of re-tweet network measures to predict whether a hashtag volume will breakout or not. We also developed a visualization tool to help trace the evolution of hashtag volumes, their underlying networks and both local and global network measures. We trained a random forest tree classifier to identify effective network measures for predicting hashtag volume breakouts. Our experiments showed that “local” network features, based on a fixed-sized sliding window, have an overall predictive accuracy of 76 %, where as, when we incorporate “global” features that utilize all interactions up to the current period, then the overall predictive accuracy of a sliding window based breakout predictor jumps to 83 %.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
Research of usability of Mashup Tools done for Kent County Council as part of the Pic and Mix Pilot (2009), opening up Kent related datasets for all to use and exploit.
Two Studies on Twitter Networks and Tweet Content in #ALS/#MND #HIC16Bronwyn Hemsley
Two Studies on Twitter Networks and Tweet Content in Relation to Amyotrophic Lateral Sclerosis (ALS): Conversation, Information, and ‘Diary of a Daily Life’
Authors: Bronwyn Hemsley, Stuart Palmer Pages 41 - 47
DOI10.3233/978-1-61499-666-8-41
Series: Studies in Health Technology and Informatics
Ebook Volume 227: Digital Health Innovation for Consumers, Clinicians, Connectivity and Community
Abstract
To date, there is no research examining how adults with Amyotrophic Lateral Sclerosis (ALS) or Motor Neurone Disease (MND) and severe communication disability use Twitter, nor the use of Twitter in relation to ALS/MND beyond its use for fundraising and raising awareness. In this paper we (a) outline a rationale for the use of Twitter as a method of communication and information exchange for adults with ALS/MND, (b) detail multiple qualitative and quantitative methods used to analyse Twitter networks and tweet content in the our studies, and (c) present the results of two studies designed to provide insights on the use of Twitter by an adult with ALS/MND and by #ALS and #MND hashtag communities in Twitter. We will also discuss findings across the studies, implications for health service providers in Twitter, and directions for future Twitter research in relation to ALS/MND.
Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagate in networks from a global perspective.
MICROBLOGGING CONTENT PROPAGATION MODELING USING TOPIC-SPECIFIC BEHAVIORAL FA...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
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.
Who’s in the Gang? Revealing Coordinating Communities in Social MediaDerek Weber
Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campaigns rather than the small groups responsible. To reveal networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in behaviours that, in concert, execute different goal-based strategies, which we describe. Our approach is validated against two relevant datasets with ground truth data. See https://github.com/weberdc/find_hccs for code and data.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining).
Co-authored with Frank Neumann (University of Adelaide)
Identifying and Characterizing User Communities on Twitter during Crisis EventsIIIT Hyderabad
Twitter is a prominent online social media which is used to share information and opinions. Previous research has shown that current real world news topics and events dominate the discussions on Twitter. In this paper, we present a preliminary study to identify and characterize communities from a set of users who post messages on Twitter during crisis events. We present our work in progress by analyzing three major crisis events of 2011 as case studies (Hurricane Irene, Riots in England, and Earthquake in Virginia). Hurricane Irene alone, caused a damage of about 7-10 billion USD and claimed 56 lives. The aim of this paper is to identify the different user communities, and characterize them by the top central users. First, we defined a similarity metric between users based on their links, content posted and meta-data. Second, we applied spectral clustering to obtain communities of users formed during three different cri- sis events. Third, we evaluated the mechanism to identify top central users using degree centrality; we showed that the top users represent the topics and opinions of all the users in the community with 81% accuracy on an average. The top central people identified represent what the entire community shares. Therefore to understand a community, we need to monitor and analyze only these top users rather than all the users in a community.
Social network has become so popular with overwhelming high rate of growth, due to this popularity the online social networks is facing the issues of spamming, which has leads to unsubstantial economic loss to this menace of spam and spammers activities. It has leads to uncontrollable dissemination of viruses and malwares, promotional ads, phishing, and scams. spam activities has enter a new dangerous dimension, the spammers have step up their games and tactics online social networks, it consumes large amounts of network bandwidth leading to less revenue and significant economic loss to both private and public sectors. From the previous scholars work on spammer classification taxonomy, various machine learning techniques have been extensively used to detect spam activities and spammers in online social networks. There are various classifier that are learn over content-based features extracted from the user's interactions and profiles to label them as spam/spammers or legitimate. But recently, new network structural bench mark features have been proposed for spammer detection task, but their importance using structural bench mark learning methods has not been extensively evaluated yet. In this research work, we evaluate the the metric performance of some structural bench mark learning methods using scientific and strategic approach based attributes extracted from an interaction network for the task of spammer detection in online social network.
Towards a More Holistic Approach on Online Abuse and AntisemitismIIIT Hyderabad
In our first work, we take a holistic approach towards analysing the different forms of abusive behaviours found in the web communities. We introduce three abuse detection tasks -- 1) presence of abuse, 2) severity of abuse, 3) target of abuse. Due to the absence of a rich abuse-based dataset of considerable size, labeled across all aspects -- presence, severity, and target, we provide a corpus with 7,601 posts collected from a popular alt-right social media platform Gab, each of which is manually labeled comprehensively across all such aspects. We also propose a Transformer based text classifier which outperforms the existing baselines on each of the three proposed tasks on the presented corpus. Our proposed classifier obtains an accuracy of 80% for abuse presence, 82% for abuse target detection, and 64% for abuse severity detection.
To the best of our knowledge, both of the presented works are first in the respective directions. Through our studies we aim to lay foundation for future research works to explore the area of hate speech and online abuse in a more holistic and complete manner.
Research of usability of Mashup Tools done for Kent County Council as part of the Pic and Mix Pilot (2009), opening up Kent related datasets for all to use and exploit.
Two Studies on Twitter Networks and Tweet Content in #ALS/#MND #HIC16Bronwyn Hemsley
Two Studies on Twitter Networks and Tweet Content in Relation to Amyotrophic Lateral Sclerosis (ALS): Conversation, Information, and ‘Diary of a Daily Life’
Authors: Bronwyn Hemsley, Stuart Palmer Pages 41 - 47
DOI10.3233/978-1-61499-666-8-41
Series: Studies in Health Technology and Informatics
Ebook Volume 227: Digital Health Innovation for Consumers, Clinicians, Connectivity and Community
Abstract
To date, there is no research examining how adults with Amyotrophic Lateral Sclerosis (ALS) or Motor Neurone Disease (MND) and severe communication disability use Twitter, nor the use of Twitter in relation to ALS/MND beyond its use for fundraising and raising awareness. In this paper we (a) outline a rationale for the use of Twitter as a method of communication and information exchange for adults with ALS/MND, (b) detail multiple qualitative and quantitative methods used to analyse Twitter networks and tweet content in the our studies, and (c) present the results of two studies designed to provide insights on the use of Twitter by an adult with ALS/MND and by #ALS and #MND hashtag communities in Twitter. We will also discuss findings across the studies, implications for health service providers in Twitter, and directions for future Twitter research in relation to ALS/MND.
Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagate in networks from a global perspective.
MICROBLOGGING CONTENT PROPAGATION MODELING USING TOPIC-SPECIFIC BEHAVIORAL FA...Nexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
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.
Who’s in the Gang? Revealing Coordinating Communities in Social MediaDerek Weber
Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campaigns rather than the small groups responsible. To reveal networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in behaviours that, in concert, execute different goal-based strategies, which we describe. Our approach is validated against two relevant datasets with ground truth data. See https://github.com/weberdc/find_hccs for code and data.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining).
Co-authored with Frank Neumann (University of Adelaide)
Identifying and Characterizing User Communities on Twitter during Crisis EventsIIIT Hyderabad
Twitter is a prominent online social media which is used to share information and opinions. Previous research has shown that current real world news topics and events dominate the discussions on Twitter. In this paper, we present a preliminary study to identify and characterize communities from a set of users who post messages on Twitter during crisis events. We present our work in progress by analyzing three major crisis events of 2011 as case studies (Hurricane Irene, Riots in England, and Earthquake in Virginia). Hurricane Irene alone, caused a damage of about 7-10 billion USD and claimed 56 lives. The aim of this paper is to identify the different user communities, and characterize them by the top central users. First, we defined a similarity metric between users based on their links, content posted and meta-data. Second, we applied spectral clustering to obtain communities of users formed during three different cri- sis events. Third, we evaluated the mechanism to identify top central users using degree centrality; we showed that the top users represent the topics and opinions of all the users in the community with 81% accuracy on an average. The top central people identified represent what the entire community shares. Therefore to understand a community, we need to monitor and analyze only these top users rather than all the users in a community.
Social network has become so popular with overwhelming high rate of growth, due to this popularity the online social networks is facing the issues of spamming, which has leads to unsubstantial economic loss to this menace of spam and spammers activities. It has leads to uncontrollable dissemination of viruses and malwares, promotional ads, phishing, and scams. spam activities has enter a new dangerous dimension, the spammers have step up their games and tactics online social networks, it consumes large amounts of network bandwidth leading to less revenue and significant economic loss to both private and public sectors. From the previous scholars work on spammer classification taxonomy, various machine learning techniques have been extensively used to detect spam activities and spammers in online social networks. There are various classifier that are learn over content-based features extracted from the user's interactions and profiles to label them as spam/spammers or legitimate. But recently, new network structural bench mark features have been proposed for spammer detection task, but their importance using structural bench mark learning methods has not been extensively evaluated yet. In this research work, we evaluate the the metric performance of some structural bench mark learning methods using scientific and strategic approach based attributes extracted from an interaction network for the task of spammer detection in online social network.
Towards a More Holistic Approach on Online Abuse and AntisemitismIIIT Hyderabad
In our first work, we take a holistic approach towards analysing the different forms of abusive behaviours found in the web communities. We introduce three abuse detection tasks -- 1) presence of abuse, 2) severity of abuse, 3) target of abuse. Due to the absence of a rich abuse-based dataset of considerable size, labeled across all aspects -- presence, severity, and target, we provide a corpus with 7,601 posts collected from a popular alt-right social media platform Gab, each of which is manually labeled comprehensively across all such aspects. We also propose a Transformer based text classifier which outperforms the existing baselines on each of the three proposed tasks on the presented corpus. Our proposed classifier obtains an accuracy of 80% for abuse presence, 82% for abuse target detection, and 64% for abuse severity detection.
To the best of our knowledge, both of the presented works are first in the respective directions. Through our studies we aim to lay foundation for future research works to explore the area of hate speech and online abuse in a more holistic and complete manner.
Website nightmares | Brenda Cordova | Web DesignBrenda Cordova
Searched online for examples of bad web design. After looking at them you will find out why having a well designed website its important. Small businesses should definitely consider hire a professional
This sermon will explore the church of Ephesus, a church that was zealous for truth, but lost it's love for Christ in fighting the good fight. We will see that love and zeal are two essential "God-rails"; if we lose either one, we spiritually crash.
Modeling Spread of Disease from Social InteractionsPrashanth Selvam
Research paper explanation of Modeling Spread of Disease from Social Interactions. A well defined PowerPoint with details on the methodology used and the observations made.
Dr. Bryan Lewis and Dr. Madhav Marathe (both at Virginia Tech) will present a data driven multi-scale approach for modeling the Ebola epidemic in West Africa. We will discuss how the models and tools were used to study a number of important analytical questions, such as:
(i) computing weekly forecasts, (ii) optimally placing emergency treatment units and more generally health care facilities, and (iii) carrying out a comprehensive counter-factual analysis related to allocation of scarce pharmaceutical and non-pharmaceutical resources. The role of big-data and behavioral adaptation in developing the computational models will be highlighted.
Massively Parallel Simulations of Spread of Infectious Diseases over Realisti...Subhajit Sahu
Highlighted notes while preparing for project on Computational Epidemics:
Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks
Abhinav Bhatele, Jae-Seung Yeom, Nikhil Jain, Chris J. Kuhlman, Yarden Livnat, Keith R. Bisset, Laxmikant V. Kale, Madhav V. Marathe
Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reactiondiffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of realtime epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts).
Massively Parallel Simulations of Spread of Infectious Diseases over Realisti...Subhajit Sahu
Highlighted notes while studying for project work:
Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks
Abhinav Bhatele†
Jae-Seung Yeom†
Nikhil Jain†
Chris J. Kuhlman∗
Yarden Livnat‡
Keith R. Bisset∗
Laxmikant V. Kale§
Madhav V. Marathe∗
†Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, California 94551 USA
∗Biocomplexity Institute & Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061 USA
‡Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah 84112 USA
§Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 USA
E-mail: †{bhatele, yeom2, nikhil}@llnl.gov, ∗{ckuhlman, kbisset, mmarathe}@vbi.vt.edu
Abstract—Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reactiondiffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of realtime epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts).
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
Epidemiological Modeling of News and Rumors on TwitterParang Saraf
Abstract: Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
Information Contagion through Social Media: Towards a Realistic Model of the ...Axel Bruns
Paper by Axel Bruns, Patrik Wikström, Peta Mitchell, Brenda Moon, Felix Münch, Lucia Falzon, and Lucy Resnyansky presented at the ACSPRI 2016 conference, Sydney, 19-22 July 2016/
Lessons from COVID-19: How Are Data Science and AI Changing Future Biomedical...Jake Chen
: COVID-19 has profoundly impacted all our lives. Not all such impacts in science are negative. For example, how we adapt to online learning, remote mentorship, and online teamwork may become new “norms” of future scientific collaborations, breaking down institutional boundaries to communication. The COVID-19 pandemic has united the scientific community more than ever, through more than 3600 clinical trials, 60,000 peer-reviewed publications, 80,000 SARS-CoV-2 genome sequences, 100,000 COVID-19 open software tools, and a global community of scientists, with which all of us are working hard to find epidemiological patterns, diagnosis, therapeutics, and vaccines in a “War Against COVID-19”. In this talk, I will define and characterize data-driven medicine primarily through my personal journey in the past ten months, having witnessed the rapid “weaponizing of data science tools” in our community’s fight against COVID-19 (including ours, at http://covid19.ubrite.org/). I will review up-to-date COVID-19 literature, especially those related to how biomedical informatics, data science, and artificial intelligence have been applied in accelerating COVID-19 breakthrough discoveries, from basic research to clinical practice. I will end by sharing my thoughts on how the future of medicine in cancer and other translational areas can benefit from the proactive incorporation of new “data science engines.”
A MACHINE LEARNING ENSEMBLE MODEL FOR THE DETECTION OF CYBERBULLYINGijaia
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Comprehensive Social Media Security Analysis & XKeyscore Espionage TechnologyCSCJournals
Social networks can offer many services to the users for sharing activities events and their ideas. Many attacks can happened to the social networking websites due to trust that have been given by the users. Cyber threats are discussed in this paper. We study the types of cyber threats, classify them and give some suggestions to protect social networking websites of variety of attacks. Moreover, we gave some antithreats strategies with future trends.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
Social network analysis and audience segmentation, presented by Jason BaldridgeSocialMedia.org
In his Brands-Only Summit Pre-Conference presentation, People Pattern's Jason Baldridge explains how profile analytics and user segmentation enables more effective product campaigns.
He covers recent results on measuring bias at scale, the effect of network structure on virality, and inferring networks from information cascades.
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
1. Analyzing social media to characterize HIV at-risk populations among MSM in San Diego
Narendran Thangarajan1, Dr. Nella Green3, Dr. Amarnath Gupta2, Dr. Susan Little3, Dr. Nadir Weibel1
Digital
Epidemiology
This research is funded by Frontier of Innovative Scholars Program, UCSD
and Center for AIDS Research, UCSD
1 Department of CSE, UC San Diego,
2 San Diego Supercomputer Center, 3 School of Medicine, UC San Diego
naren@ucsd.edu
35 MILLION people with AIDS worldwide.
1.2 MILLION people with AIDS in US.
660,000 total deaths caused by AIDS in US.
78% of the new infections in 2010 were MSM.
California (along with Florida) had the
highest number of HIV diagnoses in 2013.
Interesting recent trend - Proliferation of social networks and
real-time communication capabilities.
FISP CFAR
+ =
“Just treated a HIV infected person from location X.We should
probably conduct a PrEP intervention at X.”
“We should deploy peer education in locationY, most of our
patients are from there.”
Ineffective prevention strategies: 50,000 new HIV infections each year.Problem
Characterize and identify HIV at-risk MSM populations by studying
user sentiments and behaviors on social networks.
2015
2012
Salathé et. al. published “Digital Epidemiology” in PLoS
Computational Biology Journal
Solution
2014
Ginsberg et. al. published “Detecting influenza epidemics using
search engine query data” in Nature journal.
2008
Methods of using real-time social media technologies for
detection and remote monitoring of HIV outcomes - Sean D.
Young et. al., Elsevier Preventive Medicine, 2014.
Unraveling Abstinence and Relapse: Smoking Cessation
Reflected in Social Media - Dr. Elizabeth Murnane, CHI 2014.
1. Data collection, classification and refinementMethod
• Tweets are collected in real-time
through theTwitter Streaming API.
Twitter’s “filter hose” is used to collect
tweets from San Diego county.
• Each tweet is cleaned by removing
stop words, punctuations and
converting to lower case.
III. Migration from raw twitter data to social network graph
II. Improving the accuracy of HIV risk tweets classification using machine learning
To improve the accuracy of HIV
risk tweets classified, we
evaluated two linear classifiers -
SupportVector Machines (SVM)
and Logistic Regression with
different sets of features.
Feature Set SVM Logistic Regression
Bag of Words 15.73% 15.72%
Stop Word Removal 12.9% 12.98%
Domain Specific Terms 11.37% 7.42%
Tweeter information 17.12% 15.23%
Error rates using different linear classifiers
• The property graph model was
adopted as the data model for HIV at-
risk MSM twitter social network.
• 7 node types and 9 edge types were
identified as shown.
• Ontologies (shown in green) are used
to infer indirect relationships between
entities. For instance, it allows us to
query for users who post tweets
related to meth and sex venues.
• The resulting graph was materialized
in a graph database called Neo4J.
Results obtained using EDA queriesAnalysis
Exploratory Data Analysis queries helped understand the hidden patterns in
the HIV at-risk social network.
Querying the social graph to identify interesting communication structuresResults
Currently, we have a query-able HIV at-risk twitter network graph.
Proximity: How close are drug bucket
users to other homosexual bucket users in
terms of hop count?
Topics of interest: What are the main topics
in the discussions among people who are at
a one-hop following distance from their sub-
graph’s hubs?
Conversations: How many conversations
are happening among drug bucket users
alone , sex bucket users alone and across
drug bucket users and sex bucket users?”
Preferences: Identify two drug bucket users
who are most consulted by homosexual
people.
Current status and future worksFuture
(0) Drug (1) Homosexual (2) STI
(3) Sex (4) SexVenues
The HIV at-risk MSM social network
coupled with the real-world
HIV transmission network inferred using
phylodynamics from SD PIC will help us
understand if the actual sexual network can
be reconstructed using the social network.
Ultimately, this social network could predict
an individual’s future HIV transmission risk
enabling us to prevent it in real-time.
• Each tweet is classified as a HIV risk tweet if it falls in one
of the five HIV risk categories - Drug, SexVenues, Sex,
Homosexual, SexuallyTransmitted Infections.
• Classified tweets are refined further using exclusion and
inclusion lists of co-occurring words. e.g.“ice cold” doesn't
refer to meth (a drug commonly called “ice")
• After getting a refined set of HIV risk tweets, the relevant metadata (like tweeters
and the mentioned users) were fetched usingTwitter’s public APIs.
• Retweet and reply chains were pulled in recursively to ensure the original tweet
and the corresponding tweeter were part of the resulting social network graph.
Most active time of the day Most active day of the week Power-law distribution of tweets
Length of HIV risk tweets Tweets distribution across risk buckets Most co-occurring risk categories
• IRB approval and recruitment - Currently, we are collecting
twitter handles of people in the HIV transmission network and
those at risk of acquiring HIV. This enables us to compare the
structural similarities in the sexual network and the twitter
social network.
• Interactive data visualizations to enable visualizing the evolving
HIV at-risk social network to decipher underlying patterns in
network structure evolution and the corresponding changes in
SNA metrics.
• Computational model that captures the behavior of a HIV at-
risk user onTwitter.
Social
Network
Sexual
Network
• Collaboration with Harvard to identify change-points in the social
network structure.