Physical Sciences and Mathematics Commons^™

A Framework To Understand Emoji Meaning: Similarity And Sense Disambiguation Of Emoji Using Emojinet, Sanjaya Wijeratne

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Pictographs, commonly referred to as `emoji’, have become a popular way to enhance electronic communications. They are an important component of the language used in social media. With their introduction in the late 1990’s, emoji have been widely used to enhance the sentiment, emotion, and sarcasm expressed in social media messages. They are equally popular across many social media sites including Facebook, Instagram, and Twitter. In 2015, Instagram reported that nearly half of the photo comments posted on Instagram contain emoji, and in the same year, Twitter reported that the `face with tears of joy’ emoji has been tweeted 6.6 …

What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, And Prevention, Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William L. Romine, Amit Sheth

Kno.e.sis Publications

Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus.

Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment.

Methods: A combination of natural language processing and machine learning techniques was used to determine what people were …

Semi-Supervised Approach To Monitoring Clinical Depressive Symptoms In Social Media, Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

Computer Science and Engineering Faculty Publications

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms …

Harassment Detection On Twitter Using Conversations, Venkatesh Edupuganti

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Social media has brought people closer than ever before, but the use of social media has also brought with it a risk of online harassment. Such harassment can have a serious impact on a person such as causing low self-esteem and depression. The past research on detecting harassment on social media is primarily based on the content of messages exchanged on social media. The lack of context when relying on a single social media post can result in a high degree of false alarms. In this study, I focus on the reliable detection of harassment on Twitter by better understanding …

Personalized And Adaptive Semantic Information Filtering For Social Media, Pavan Kapanipathi

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Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media. Building a personalized filtering system involves understanding users' interests and utilizing these interests to …

Mining And Analyzing Subjective Experiences In User Generated Content, Lu Chen

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Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality …

Intent Classification Of Short-Text On Social Media, Hemant Purohit, Guozhu Dong, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis …

On Using Synthetic Social Media Stimuli In An Emergency Preparedness Functional Exercise, Andrew Hampton, Shreyansh Bhatt, Gary Alan Smith, Jeremy S. Brunn, Hemant Purohit, Valerie L. Shalin, John M. Flach, Amit P. Sheth

Kno.e.sis Publications

This paper details the creation and use of a massive (over 32,000 messages) artificially constructed 'Twitter' microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. PIOs searched, monitored, and (through conventional channels) verified potentially actionable information that could then be redistributed through a personalized screen name. Our case study of a key PIO reveals several capabilities that social …

Gender-Based Violence In 140 Characters Or Fewer: A #Bigdata Case Study Of Twitter, Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit P. Sheth

Kno.e.sis Publications

Public institutions are increasingly reliant on data from social media sites to measure public attitude and provide timely public engagement. Such reliance includes the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly fourteen million tweets collected from Twitter over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events …

Features For Ranking Tweets Based On Credibility And Newsworthiness, Jacob W. Ross

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We create a robust and general feature set for learning to rank algorithms that rank tweets based on credibility and newsworthiness. In previous works, it has been demonstrated that when the training and testing data are from two distinct time periods, the ranker performs poorly. We improve upon previous work by creating a feature set that does not over fit a particular year or set of topics. This is critical given how people utilize social media changes as time progresses, and the topics discussed vary. In addition, we are constantly gaining new tweet data. Thus, it is important to be …

Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit

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Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including 'articulation' …

Discovering Perceptions In Online Social Media: A Probabilistic Approach, Derek Doran, Swapna S. Gokhale, Aldo Dagnino

Kno.e.sis Publications

People across the world habitually turn to online social media to share their experiences, thoughts, ideas, and opinions as they go about their daily lives. These posts collectively contain a wealth of insights into how masses perceive their surroundings. Therefore, extracting people’s perceptions from social media posts can provide valuable information about pertinent issues such as public transportation, emergency conditions, and even reactions to political actions or other activities. This paper proposes a novel approach to extract such perceptions from a corpus of social media posts originating from a given broad geographical region. The approach divides the broad region into …

Assisting Coordination During Crisis: A Domain Ontology Based Approach To Infer Resource Needs From Tweets, Shreyansh Bhatt, Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach

Kno.e.sis Publications

Ubiquitous social media during crises provides citizen reports on the situation, needs and supplies. Previous research extracts resource needs directly from the text (e.g. "Power cut to Coney Island and Brighton beach" indicates a power need). This approach assumes that citizens derive and write about specific needs from their observations, properly specified for the emergency response system, an assumption that is not consistent with general conversational behavior. In our study, Twitter messages (tweets) from Hurricane Sandy in 2012 clearly indicate power blackouts, but not their probable implications (e.g. loss of power to hospital life support systems). We use a domain …

Cursing In English On Twitter, Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people's cursing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform - Twitter, involving the analysis of …

Crisis Response Coordination In Online Communities, Hemant Purohit

Kno.e.sis Publications

During recent crises, citizens (sensors) are increasingly using social media to share variety of information- situation on the ground, emerging needs, donation offers, damage, etc. In such an evolving ad-hoc community, how can we extract actionable nuggets from the social media streams to aid relief efforts? This doctoral consortium presentation summarizes a framework to analyze social data and manage information to assist coordination by focusing on three important questions to answer: Whom to coordinate with, Why to coordinate and How to coordinate, with exemplary insights for needs and availability from the recent disaster events.

What Kind Of #Communication Is Twitter? A Psycholinguistic Perspective On Communication In Twitter For The Purpose Of Emergency Coordination, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach

Kno.e.sis Publications

The present research aims to detect coordinated citizen response within social media traffic to assist emergency response. We use domain-independent linguistic properties as the first step in narrowing the candidate set of messages for domain-dependent and computationally intensive analysis.

Framework For The Analysis Of Coordination In Crisis Response, Hemant Purohit, Andrew Hampton, Valerie L. Shalin, Amit P. Sheth, John M. Flach

Kno.e.sis Publications

Social Media play a critical role during crisis events, revealing a natural coordination dynamic. We propose a computational framework guided by social science principles to measure, analyze, and understand coordination among the different types of organizations and actors in crisis response. The analysis informs both the scientific account of cooperative behavior and the design of applications and protocols to support crisis management.

Dynamic Associative Relationships On The Linked Open Data Web, Pablo N. Mendes, Pavan Kapanipathi, Delroy H. Cameron, Amit P. Sheth

Kno.e.sis Publications

We provide a definition of context based on theme, time and location, and propose a mixed retrieval/extraction model for the dynamic suggestion of trending relationships to LOD resources.

Linked Open Social Signals, Pablo N. Mendes, Alexandre Passant, Pavan Kapanipathi, Amit P. Sheth

Kno.e.sis Publications

In this paper we discuss the collection, semantic annotation and analysis of real-time social signals from micro-blogging data. We focus on users interested in analyzing social signals collectively for sensemaking. Our proposal enables flexibility in selecting subsets for analysis, alleviating information overload. We define an architecture that is based on state-of-the-art Semantic Web technologies and a distributed publish subscribe protocol for real time communication. In addition, we discuss our method and application in a scenario related to the health care reform in the United States.

Understanding User-Generated Content On Social Media, Bala Meenakshi Nagarajan

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Over the last few years, there has been a growing public and enterprise fascination with 'social media' and its role in modern society. At the heart of this fascination is the ability for users to participate, collaborate, consume, create and share content via a variety of platforms such as blogs, micro-blogs, email, instant messaging services, social network services, collaborative wikis, social bookmarking sites, and multimedia sharing sites.

This dissertation is devoted to understanding informal user-generated textual content on social media platforms and using the results of the analysis to build Social Intelligence Applications.

The body of research presented in this …