Shamanth Kumar

Understanding Twitter data with TweetXplorer

In the era of big data it is increasingly difficult for an analyst to extract meaningful knowledge from a sea of information. We present TweetXplorer, a system for analysts with little information about an event to gain knowledge through the use of effective visualization techniques. Using tweets collected during Hurricane Sandy as an example, we will lead the reader through a workflow that exhibits the functionality of the system.

Lessons learned in using social media for disaster relief - ASU crisis response game

In disasters such as the earthquake in Haiti and the tsunami in Japan, people used social media to ask for help or report injuries. The popularity, efficiency, and ease of use of social media has led to its pervasive use during the disaster. This creates a pool of timely reports about the disaster, injuries, and help requests. This offers an alternative opportunity for first responders and disaster relief organizations to collect information about the disaster, victims, and their needs. It also presents a challenge for these organizations to aggregate and process the requests from different social media. Given the sheer volume of requests, it is necessary to filter reports and select those of high priority for decision making. Little is known about how the two phases should be smoothly integrated. In this paper we report the use of social media during a simulated crisis and crisis response process, the ASU Crisis Response Game. Its main objective is to creat a training capability to understand how to use social media in crisis. We report lessons learned from this exercise that may benefit first responders and NGOs who use social media to manage relief efforts during the disaster.

Visualizing Social Media Sentiment in Disaster Scenarios

Recently, social media, such as Twitter, has been successfully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media during disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social media streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualization of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geographic visualization. In particular, we provide an entropy-based metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how visual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.

A behavior analytics approach to identifying tweets from crisis regions

The growing popularity of Twitter as an information medium has allowed unprecedented access to first-hand information during crises and mass emergency situations. Due to the sheer volume of information generated during a disaster, a key challenge is to filter tweets from the crisis region so their analysis can be prioritized. In this paper, we introduce the task of identifying whether a tweet is generated from crisis regions and formulate it as a decision problem. This problem is challenging due to the fact that only ~1% of all tweets have location information. Existing approaches tackle this problem by predicting the location of the user using historical tweets from users or their social network. As collecting historical information is not practical during emergency situations, we investigate whether it is possible to determine that a tweet originates from the crisis region through the information in the tweet and the publishing users profile.

Navigating information facets on twitter (NIF-T)

Recent years have seen an exponential increase in the number of users of social media sites. As the number of users of these sites continues to grow at an extraordinary rate, the amount of data produced follows in magnitude. With this deluge of social media data, the need for comprehensive tools to analyze user interactions is ever increasing. In this paper, we present a novel tool, Navigating Information Facets on Twitter (NIF-T), which helps users to explore data generated on social media sites. Using the three dimensions or facets: time, location, and topic as an example of the many possible facets, we enable the users to explore large social media datasets. With the help of a large corpus of tweets collected from the Occupy Wall Street movement on the Twitter platform we show how our system can be used to identify important aspects of the event along these facets.

Convergence of influential bloggers for topic discovery in the blogosphere

In this paper, we propose a novel approach to automatically detect “hot” or important topics of discussion in the blogosphere. The proposed approach is based on analyzing the activity of influential bloggers to determine specific points in time when there is a convergence amongst the influential bloggers in terms of their topic of discussion. The tool BlogTrackers, is used to identify influential bloggers and the Normalized Google Distance is used to define the similarity amongst the topics of discussion of influential bloggers. The key advantage of the proposed approach is its ability to automatically detect events which are important in the blogger community.

Analyzing Behavior of the Influentials Across Social Media

The popularity of social media as an information source, in the recent years has spawned several interesting applications, and consequently challenges to using it effectively. Identifying and targeting influential individuals on sites is a crucial way to maximize the returns of advertising and marketing efforts. Recently, this problem has been well studied in the context of blogs, microblogs, and other forms of social media sites. Understanding how these users behave on a social media site and even across social media sites will lead to more effective strategies. In this book chapter, we present existing techniques to identify influential individuals in a social media site. We present a user identification strategy, which can help us to identify influential individuals across sites. Using a combination of these approaches we present a study of the characteristics and behavior of influential individuals across sites. We evaluate our approaches on several of the popular social media sites. Among other interesting findings, we discover that influential individuals on one site are more likely to be influential on other sites as well. We also find that influential users are more likely to connect to other influential individuals.

Exploring a Scalable Solution to Identifying Events in Noisy Twitter Streams

The unprecedented use of social media through smartphones and other web-enabled mobile devices has enabled the rapid adoption of platforms like Twitter. Event detection has found many applications on the web, including breaking news identification and summarization. The recent increase in the usage of Twitter during crises has attracted researchers to focus on detecting events in tweets. However, current solutions have focused on static Twitter data. The necessity to detect events in a streaming environment during fast paced events such as a crisis presents new opportunities and challenges. In this paper, we investigate event detection in the context of real-time Twitter streams as observed in real-world crises. We highlight the key challenges in this problem: the informal nature of text, and the high-volume and high-velocity characteristics of Twitter streams. We present a novel approach to address these challenges using single-pass clustering and the compression distance to efficiently detect events in Twitter streams. Through experiments on large Twitter datasets, we demonstrate that the proposed framework is able to detect events in near real-time and can scale to large and noisy Twitter streams.

Visualizing Twitter Data

Twitter® is a massive social networking site tuned towards fast communication. More than 140 million active users publish over 400 million 140-character “Tweets” every day.

Analyzing Twitter Data

So far we have discussed the collection and management of a large set of Tweets. It is time to put these Tweets to work to gain information about the data we have collected. This chapter focuses on two key aspects of Twitter data for data analysis: networks and text.