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A Twitter-Based Study For Understanding Public Reaction On Zika Virus, Roopteja Muppalla
A Twitter-Based Study For Understanding Public Reaction On Zika Virus, Roopteja Muppalla
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In recent times, social media platforms like Twitter have become more popular and people have become more interactive and responsive than before. People often react to every news in real-time and within no-time, the information spreads rapidly. Even with viral diseases like Zika, people tend to share their opinions and concerns on social media. This can be leveraged by the health officials to track the disease in real-time thereby reducing the time lag due to traditional surveys. A faster and accurate detection of the disease can allow health officials to understand people's opinion of the disease and take necessary precautions …
A Framework To Understand Emoji Meaning: Similarity And Sense Disambiguation Of Emoji Using Emojinet, Sanjaya Wijeratne
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 …
Content-Based Clustering And Visualization Of Social Media Text Messages, Sydney A. Barnard
Content-Based Clustering And Visualization Of Social Media Text Messages, Sydney A. Barnard
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Although Twitter has been around for more than ten years, crisis management agencies and first response personnel are not able to fully use the information this type of data provides during a crisis or natural disaster. This thesis addresses clustering and visualizing social media data by textual similarity, rather than by only time and location, as a tool for first responders. This thesis presents a tool that automatically clusters geotagged text data based on their content and displays the clusters and their locations on the map. It allows at-a-glance information to be displayed throughout the evolution of a crisis. For …
A Semantically Enhanced Approach To Identify Depression-Indicative Symptoms Using Twitter Data, Ankita Saxena
A Semantically Enhanced Approach To Identify Depression-Indicative Symptoms Using Twitter Data, Ankita Saxena
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According to the World Health Organization, more than 300 million people suffer from Major Depressive Disorder (MDD) worldwide. PHQ-9 is used to screen and diagnose MDD clinically and identify its severity. With the unprecedented growth and enthusiastic acceptance of social media such as Twitter, a large number of people have come to share their feelings and emotions on it openly. Each tweet can indicate a user's opinion, thought or feeling. A tweet can also indicate multiple symptoms related to PHQ-9. Identifying PHQ-9 symptoms indicated by a tweet can provide crucial information about a user regarding his/her depression diagnosis. The current …
Harassment Detection On Twitter Using Conversations, Venkatesh Edupuganti
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
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 …
Social Health Signals, Ashutosh Sopan Jadhav, Swapnil Soni, Amit P. Sheth
Social Health Signals, Ashutosh Sopan Jadhav, Swapnil Soni, Amit P. Sheth
Kno.e.sis Publications
Recently Twitter, has emerged as one of the primary medium for sharing and seeking of the latest information related to variety of the topics including health information. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, identification of useful information from the deluge of tweets is one of the major challenge. Twitter search is limited to keyword based techniques to retrieve information for a given query and sometimes the results do not contain real-time information. Moreover, …
Domain Specific Document Retrieval Framework For Real-Time Social Health Data, Swapnil Soni
Domain Specific Document Retrieval Framework For Real-Time Social Health Data, Swapnil Soni
Kno.e.sis Publications
With the advent of the web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these online services to share and seek health real-time information has in- creased exponentially. OHIS use web search engines or microblogging search services to seek out latest, relevant as well as reliable health in- formation. When OHIS turn to microblogging search services to search real-time content, trends and breaking news, etc. the search results are not promising. Two major challenges exist in the current microblogging search engines are keyword based techniques and results do not contain real-time information. To address these challenges, …
Domain Specific Document Retrieval Framework On Near Real-Time Social Health Data, Swapnil Soni
Domain Specific Document Retrieval Framework On Near Real-Time Social Health Data, Swapnil Soni
Kno.e.sis Publications
With the advent of web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these services to share and seek health information in real-time has increased exponentially. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking of the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, the identification of useful information from the deluge of tweets is one of the major challenges. Twitter search is limited to keyword-based techniques to retrieve information for a given query and sometimes the results do not …
Knowledge Enabled Location Prediction Of Twitter Users, Revathy Krishnamurthy
Knowledge Enabled Location Prediction Of Twitter Users, Revathy Krishnamurthy
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As the popularity of online social networking sites such as Twitter and Facebook continues to rise, the volume of textual content generated on the web is increasing rapidly. The mining of user generated content in social media has proven effective in domains ranging from personalization and recommendation systems to crisis management. These applications stand to be further enhanced by incorporating information about the geo-position of social media users in their analysis. Due to privacy concerns, users are largely reluctant to share their location information. As a consequence of this, researchers have focused on automatic inferencing of location information from the …
Knowledge Enabled Approach To Predict The Location Of Twitter Users, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Knowledge Enabled Approach To Predict The Location Of Twitter Users, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Kno.e.sis Publications
Knowledge bases have been used to improve performance in applications ranging from web search and event detection to entity recognition and disambiguation. More recently, knowledge bases have been used to analyze social data. A key challenge in social data analysis has been the identification of the geographic location of online users in a social network such as Twitter. Existing approaches to predict the location of users, based on their tweets, rely solely on social media features or probabilistic language models. These approaches are supervised and require large training dataset of geo-tagged tweets to build their models. As most Twitter users …
Features For Ranking Tweets Based On Credibility And Newsworthiness, Jacob W. Ross
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 …
Domain-Specific Document Retrieval Framework For Near Real-Time Social Health Data, Swapnil Soni
Domain-Specific Document Retrieval Framework For Near Real-Time Social Health Data, Swapnil Soni
Browse all Theses and Dissertations
With the advent of web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these services to share and seek health information in real-time has increased exponentially. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking of the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, the identification of useful information from the deluge of tweets is one of the major challenges. Twitter search is limited to keyword-based techniques to retrieve information for a given query and sometimes the results do not …
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Kno.e.sis Publications
Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowdsource annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to iteratively and interactively correcting mislabeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable …
Hierarchical Interest Graph From Tweets, Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit P. Sheth
Hierarchical Interest Graph From Tweets, Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit P. Sheth
Kno.e.sis Publications
Industry and researchers have identified numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these different works is the identification of user interests. Although techniques have been developed to address this challenge, a flexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph. To create such graphs, we utilize users' tweets to first ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt …
Cursing In English On Twitter, Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan, Amit P. Sheth
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 …
Location Prediction Of Twitter Users Using Wikipedia, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Location Prediction Of Twitter Users Using Wikipedia, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
Kno.e.sis Publications
The mining of user generated content in social media has proven very effective in domains ranging from personalization and recommendation systems to crisis management. The knowledge of online users locations makes their tweets more informative and adds another dimension to their analysis. Existing approaches to predict the location of Twitter users are purely data-driven and require large training data sets of geo-tagged tweets. The collection and modelling process of tweets can be time intensive. To overcome this drawback, we propose a novel knowledge based approach that does not require any training data. Our approach uses information in Wikipedia, about cities …
User Taglines: Alternative Presentations Of Expertise And Interest In Social Media, Hemant Purohit, Alex Dow, Omar Alonso, Lei Duan, Kevin Haas
User Taglines: Alternative Presentations Of Expertise And Interest In Social Media, Hemant Purohit, Alex Dow, Omar Alonso, Lei Duan, Kevin Haas
Kno.e.sis Publications
Web applications are increasingly showing recommended users from social media along with some descriptions, an attempt to show relevancy - why they are being shown. For example, Twitter search for a topical keyword shows expert twitterers on the side for 'whom to follow'. Google+ and Facebook also recommend users to follow or add to friend circle. Popular Internet newspaper- The Huffington Post shows Twitter influencers/ experts on the side of an article for authoritative relevant tweets. The state of the art shows user profile bios as summary for Twitter experts, but it has issues with length constraint imposed by user …
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
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.
Prediction Of Topic Volume On Twitter, Yiye Ruan, Hemant Purohit, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth
Prediction Of Topic Volume On Twitter, Yiye Ruan, Hemant Purohit, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth
Kno.e.sis Publications
We discuss an approach for predicting microscopic (individual) and macroscopic (collective) user behavioral patterns with respect to specific trending topics on Twitter. Going beyond previous efforts that have analyzed driving factors in whether and when a user will publish topic-relevant tweets, here we seek to predict the strength of content generation which allows more accurate understanding of Twitter users' behavior and more effective utilization of the online social network for diffusing information. Unlike traditional approaches, we consider multiple dimensions into one regression-based prediction framework covering network structure, user interaction, content characteristics and past activity. Experimental results on three large Twitter …
Extracting Diverse Sentiment Expressions With Target-Dependent Polarity From Twitter, Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, Amit P. Sheth
Extracting Diverse Sentiment Expressions With Target-Dependent Polarity From Twitter, Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang, Amit P. Sheth
Kno.e.sis Publications
This study focuses on automatic extraction of sentiment expressions associated with given targets from Twitter. It addresses one of the key challenges in this work: Wide diversity and informal nature of sentiment expressions that cannot be trivially enumerated or captured using predefined lexical patterns.
Personalized Filtering Of The Twitter Stream, Pavan Kapanipathi, Fabrizio Orlandi, Amit P. Sheth, Alexandre Passant
Personalized Filtering Of The Twitter Stream, Pavan Kapanipathi, Fabrizio Orlandi, Amit P. Sheth, Alexandre Passant
Kno.e.sis Publications
With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss …
Linked Open Social Signals, Pablo N. Mendes, Alexandre Passant, Pavan Kapanipathi, Amit P. Sheth
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.
A Qualitative Examination Of Topical Tweet And Retweet Practices, Meenakshi Nagarajan, Hemant Purohit, Amit P. Sheth
A Qualitative Examination Of Topical Tweet And Retweet Practices, Meenakshi Nagarajan, Hemant Purohit, Amit P. Sheth
Kno.e.sis Publications
This work contributes to the study of retweet behavior on Twitter surrounding real-world events. We analyze over a million tweets pertaining to three events, present general tweet properties in such topical datasets and qualitatively analyze the properties of the retweet behavior surrounding the most tweeted/viral content pieces. Findings include a clear relationship between sparse/dense retweet patterns and the content and type of a tweet itself; suggesting the need to study content properties in link-based diffusion models.