Omar ElTayeby

Comparative case study between D3 & Highcharts on Lustre metadata visualization

One of the challenging tasks in visual analytics is to target clustered time-series data sets, since it is important for data analysts to discover patterns changing over time while keeping their focus on particular subsets. A web-based application to monitor the Lustre file system for system administrators and operation teams has been developed using D3 and Highcharts. This application is a use case to compare those JavaScript libraries demonstrating the differences in capabilities of using them. The goal of this application is to provide time-series visuals of the Remote Procedure Calls (RPCs) and storage patterns of users on Kraken, a University of Tennessee High Performance Computing (HPC) resource in Oak Ridge National Laboratory (ORNL).

Detecting Drinking-Related Contents on Social Media by Classifying Heterogeneous Data Types

One common health problem in the US faced by colleges and universities is binge drinking. College students often post drinking related texts and images on social media as a socially desirable identity. Some public health and clinical research scholars have surveyed different social media sites manually to understand their behavior patterns. In this paper, we investigate the feasibility of mining the heterogeneous data scattered on social media to identify drinking-related contents, which is the first step towards unleashing the potential of social media in automatic detection of binge drinking users. We use the state-of-the-art algorithms such as Support Vector Machine and neural networks to classify drinking from non-drinking posts, which contain not only text, but also images and videos. Our results show that combining heterogeneous data types, we are able to identify drinking related posts with an overall accuracy of 82%. Prediction models based on text data is more reliable compared to the other two models built on image and video data for predicting drinking related contents.

A Framework for Interactive Exploratory Learning Analytics

Many analytic tools have been developed to discover knowledge from student data. However, the knowledge discovery process requires advanced analytical modelling skills, making it the province of data scientists. This impedes the ability of educational leaders, professors, and advisors to engage with the knowledge discovery process directly. As a result, it is challenging for analysis to take advantage of domain expertise, making its outcome often neither interesting nor useful. Usually the outcome produced from such analytic tools is static, preventing domain experts from exploring different hypotheses by changing data models or predictive models inside the tool. We have developed a framework for interactive and exploratory learning analytics which begins to address these challenges. We engaged in data exploration and hypotheses generation with our university domain experts by conducting two focus groups. We used the findings of these focus groups to validate our framework, arguing that it enables domain experts to explore the data, analysis and interpretation of student data to discover useful and interesting knowledge.

Using Information Processing Strategies to Predict Contagion of Social Media Behavior: A Theoretical Model

This study presents the Social Media Cognitive Processing model, which explains and predicts the depth of processing on social media based on three classic concepts from the offline literature about cognitive processing: self-generation, psychological distance, and self-reference. Together, these three dimensions have tremendous explanatory power in predicting the depth of processing a receiver will have in response to a sender’s message. Moreover, the model can be used to explain and predict the direction and degree of information proliferation. This model can be used in a variety of contexts (e.g., isolating influencers to persuade others about the merits of vaccination, to dispel fake news, or to spread political messages). We developed the model in the context of Brexit tweets.

A feasibility study on identifying drinking-related contents in Facebook through mining heterogeneous data

Binge drinking is a severe health problem faced by many US colleges and universities. College students often post drinking-related text and images on social media, portraying their alcohol use as socially desirable. In this project, we investigated the feasibility of mining the heterogeneous data (e.g. text, images, and videos) on Facebook to identify drinking-related contents. We manually annotated 4266 posts during 21 October 2011 and 3 November 2014 from “I’m Shmacked” group on Facebook, where 511 posts were drinking-related. Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81. Prediction models built on text data were more reliable compared to those built on image and video data for predicting drinking-related contents. As the first step of our efforts in this direction, this feasibility study showed promise toward unleashing the potential of mining social media to identify students who binge drink.

Measuring the Influence of Mass Media on Opinion Segregation through Twitter

Abstract During the US presidential elections the media played a major role in presenting the candidates’ vision on several topics. Nevertheless, the diversity of opinions along with the political currents, one might notice segregation in opinions among some topics related to each other or a candidate. In the meanwhile, posting opinions on social media could be represented as a sentiment vector towards multiple issues. This gives us a ripe ground for clustering opinions to view tweets that hold similar opinions. In this paper we investigate the medias influence on segregating opinions by constructing an aspect-based opinion mining framework. Our main task is to detect the segregated groups of opinions by solving the proposed model using expectation maximization (EM) algorithm. We examined a corpus of tweets collected, which are related to famous political topics. We show interesting observations on the sentiment used for particular topics among the groups of opinions, and conclude the percentages of media influences among the segregated groups of opinions with respect to these topics.

Comparative case study between D3 and highcharts on lustre data visualization

One of the challenging tasks in visual analytics is to target clustered time-series data sets, since it is important for data analysts to discover patterns changing over time while keeping their focus on particular subsets. In order to leverage the humans ability to quickly visually perceive these patterns, multivariate features should be implemented according to the attributes available. However, a comparative case study has been done using JavaScript libraries to demonstrate the differences in capabilities of using them. A web-based application to monitor the Lustre file system for the systems administrators and the operation teams has been developed using D3 and Highcharts. Lustre file systems are responsible of managing Remote Procedure Calls (RPCs) which include input output (I/O) requests between clients and Object Storage Targets (OSTs). The objective of this application is to provide time-series visuals of these calls and storage patterns of users on Kraken, a University of Tennessee High Performance Computing (HPC) resource in Oak Ridge National Laboratory (ORNL).