Browsing by Subject "data visualization"

Now showing 1 - 7 of 7
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    Are Recent Terrorism Trends Reflected in Social Media?
    (IEEE, 2017-10) Terziyska, Ivana; Shah, Setu; Luo, Xiao; Engineering Technology, School of Engineering and Technology
    Social media plays an important role in shaping the beliefs and sentiments of an audience regarding an event. A comparison between public data sets that have holistic features and social media data set that include more user features would give insight into the spread of misinformation and aspects of events that are reflected in user behavior. In this research, we compare the trends identified in the public data set - Global Terrorism Database (GTD) with the trends reflected through the social media data obtained using the Twitter API. The unsupervised learning algorithm Self-Organizing Map (SOM) is used to identify the features and trends summarized by the clusters. The results show discrepancies in the features and related trends of terrorism events in the GTD data set and obtained Twitter data set to suggest some media bias and public perception on terrorism.
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    Data analytics for modeling and visualizing attack behaviors: A case study on SSH brute force attacks
    (IEEE, 2017-11) Yao, Chengchao; Luo, Xiao; Zincir-Heywood, A. Nur; Computer and Information Science, School of Science
    In this research, we explore a data analytics based approach for modeling and visualizing attack behaviors. To this end, we employ Self-Organizing Map and Association Rule Mining algorithms to analyze and interpret the behaviors of SSH brute force attacks and SSH normal traffic as a case study. The experimental results based on four different data sets show that the patterns extracted and interpreted from the SSH brute force attack data sets are similar to each other but significantly different from those extracted from the SSH normal traffic data sets. The analysis of the attack traffic provides insight into behavior modeling for brute force SSH attacks. Furthermore, this sheds light into how data analytics could help in modeling and visualizing attack behaviors in general in terms of data acquisition and feature extraction.
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    Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
    (MDPI, 2020-09) Trajkova, Milka; Alhakamy, A'aeshah; Cafaro, Francesco; Vedak, Sanika; Mallappa, Rashmi; Kankara, Sreekanth R.; Human-Centered Computing, School of Informatics and Computing
    Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.
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    Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
    (MDPI, 2020-09) Trajkova, Milka; Alhakamy, A’aeshah; Cafaro, Francesco; Vedak, Sanika; Mallappa, Rashmi; Kankara, Sreekanth R.; Human-Centered Computing, School of Informatics and Computing
    Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.
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    Visualization of Cardiac Implantable Electronic Device Data for Older Adults Using Participatory Design
    (Thieme, 2019-09-18) Ahmed, Ryan; Toscos, Tammy; Ghahari, Romisa Rohani; Holden, Richard J.; Martin, Elizabeth; Wagner, Shauna; Daley, Carly; Coupe, Amanda; Mirro, Michael; BioHealth Informatics, School of Informatics and Computing
    Patients with heart failure (HF) are commonly implanted with cardiac resynchronization therapy (CRT) devices as part of their treatment. Presently, they cannot directly access the remote monitoring (RM) data generated from these devices, representing a missed opportunity for increased knowledge and engagement in care. However, electronic health data sharing can create information overload issues for both clinicians and patients, and some older patients may not be comfortable using the technology (i.e., computers and smartphones) necessary to access this data. To mitigate these problems, patients can be directly involved in the creation of data visualization tailored to their preferences and needs, allowing them to successfully interpret and act upon their health data. We held a participatory design (PD) session with seven adult patients with HF and CRT device implants, who were presently undergoing RM, along with two informal caregivers. Working in three teams, participants used drawing supplies and design cards to design a prototype for a patient-facing dashboard with which they could engage with their device data. Information that patients rated as a high priority for the “Main Dashboard” screen included average percent pacing with alerts for abnormal pacing, other device information such as battery life and recorded events, and information about who to contact with for data-related questions. Preferences for inclusion in an “Additional Information” display included a daily pacing chart, health tips, aborted shocks, a symptom list, and a journal. These results informed the creation of an actual dashboard prototype which was later evaluated by both patients and clinicians. Additionally, important insights were gleaned regarding the involvement of older patients in PD for health technology.
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    Visualizing Social Science Research in an Institutional Repository
    (2015-06-03) Polley, David E.
    Using text mining and visualization techniques to identify the topical coverage of text corpora is increasingly common in a number of disciplines. When these approaches are applied to the titles and abstracts of articles published in an academic journal, it yields insight into the evolution of scholarly content in the journal. Similarly, text mining and visualization can reveal the topical coverage of items archived in an institutional repository. This poster will present initial results from mining the text and visualizing the abstracts of social science research in one university’s institutional repository. Generating a topic map visually demonstrates how research in a repository clusters around specific domains in the social sciences. These topic maps are potentially useful to librarians and researchers seeking to learn more about the topical coverage of items in their repository and determine if the research is reflective of the scholarly output from an institution more broadly.
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    Visualizing Tweet Activity for NIH Covid-19 Preprints
    (2021-10-11) Dolan, Levi
    As a result of the Covid-19 pandemic, the focus on preprint articles as a means of rapid research dissemination has sharply intensified. Produced as part of a larger project examining public comments on preprint articles, this poster breaks down an automated method which was used to gather geographic information via an Altmetric API for the 200k+ Tweets about 1,000 of the first articles in the NIH Preprint Pilot. The workflow in gathering the information for a final visualization is explained, with the results visualized as a global map overlaid with a scatterplot.