SMART Lab

Social Media Analytics Research Team (SMART) Lab at Ohio University

Research

Rapid technological change has led to the generation of vast amounts of data that holds immense value and has the potential to provide meaningful insights. When captured, understood and visualized, such data can help solve problems being faced by organizations and businesses and enable them to better serve their communities, enhance their brands, and build stronger relationships. Social media analytics research is exciting and applies in areas such as Health Communication, Politics, Business, Psychology, Computer Science, and Journalism.

Human behavior in online communities can be better understood through a range of analytics tools by extracting hidden patterns and information. We use both qualitative and quantitative research methods to provide solutions for complex problems facing organizations and businesses.

Research Clusters

The SMART Lab research clusters at Ohio University are an inter-disciplinary effort to mobilize interdepartmental strengths to provide innovative spaces for research collaborations across various application domains. This endeavor is vital for students and faculty to engage in impactful interdisciplinary research.  SMART Lab research clusters are providing a mechanism to connect scholars from different disciplines in order to foster and promote cutting-edge research. 

The challenges being faced in the world require a concerted effort by scholars in different research domains. The answers to various problems require a broader and wholesome understanding that capitalizes on the unique strengths of diverse disciplines.  Through building collaborations, the SMART Lab helps brainstorm research ideas in today’s convergent media environments, supports members’ ongoing work, and organizes discussion sessions related to members’ projects. All these initiatives promote a culture of meaningful and inclusive research. 

SMART Lab active research clusters are currently organized and focused on four broad themes. These are (i) learning analytics (ii) digital inequality and media literacies (iii) media bias and the representation of minorities, and (iv) Audience engagement for health, crisis, and sports. All these research clusters are headed by our lab director, Dr. Laeeq Khan.

Learning Analytics Research Cluster 

The learning analytics (LA) research cluster has been organized to encourage collection, measurement, and analysis of the data for visual reporting to gain more insights into in the behavior of learners and their environment.  Learning analytics employs computational analysis and reporting of data on learners for the purposes of understanding learners, optimizing learning and the environment in which learning occurs. Leaning analytics compiles large amount of data about students from a complex network of information for better understanding of learner goals and supports in the improvement of learning contents for better pedagogy. This type of analytics assists in finding out what courses and materials engages and matters the most to learners, identify and fill the gaps in courses based on the needs of learners and determine how specific course objectives are being met by these learners as they study the course contents.  LA involves a series of academic sub-domains, including but is not limited to, social media analytics, opinion mining, teaching evaluations, and academic analytics. 

Digital inequality and Media Literacies Research Cluster

This research cluster brings together faculty and students from various educational backgrounds with interest in new media and the online spread of misinformation. The objective is to employ theoretical and methodological approaches and explore and discuss the recent phenomena in disparities in media access and use, efficient use of social media, media literacies and understanding of misinformation, and disinformation. Members in the NMSM research cluster engage in sharing knowledge, acquiring new skills, and encouraging direct involvement in collaborative-cutting-edge research.

Media Bias and Representations of Minorities Research Cluster

Media bias and representations of Minorities Research Cluster aims to bring together scholars who are interested in understanding issues related to media bias, representation of minorities, and the impacts of such biases in real life. A group of students and faculty are working collaboratively using analytics tools to advance understanding of the ‘other’ images in media utilizing mass communication theories, such as framing and agenda-setting. They also analyze online debates and people’s interactions about ingroup and outgroup media portrayals.

Focusing on the new shift to new streaming services, members involved in newly created research projects studying Netflix and Disney plus representations of the others by collecting and analyzing data generated from social media platforms and other online websites. One of the exciting components in this cluster is combining and triangulating social data analytics methods alongside different formal research methodologies.

Audience Engagement for Health, Crisis, and Sports Research Cluster

This research cluster aims to explore the quality and accuracy of online health information, public health informatics domains, and global health crises. Faculty and graduate students in the field employ different theoretical and methodological research approaches to bring considerable insights and experience in studying health informatics, persuasive health campaigns, and public health information. They are also involved in innovative research projects that explore the effectiveness of health campaigns, online health messaging, and online engagements and opinions about specific health issues.

Research Publications

  • Malik, A., Antonino, A., Khan, M. L., & Nieminen, M. (2021). Characterizing HIV discussions and engagement on Twitter. Health and Technology, 1-9. https://doi.org/10.1007/s12553-021-00577-z
  • Malik, A., Khan, M. L., & Quan-Haase A., (2021). Public Health Agencies Outreach through Instagram during COVID-19 Pandemic: Crisis and Emergency Risk Communication Perspective, International Journal of Disaster Risk Reduction, 61, 102346, https://doi.org/10.1016/j.ijdrr.2021.102346
  • Khan, M. Laeeq, Ittefaq, M., Pantoja, Y., Raziq, M., & Malik, A. (2021). Public Engagement Model to analyze digital diplomacy on Twitter: A social media analytics framework, International Journal of Communication, 15, 1741-1769, https://ijoc.org/index.php/ijoc/article/view/15698 
  • Otieno, A. W.,  Roark, J., Khan, M. Laeeq, Pant, S., Grijalva, M. J., & Titsworth, Scott, (2020). The kiss of death – Unearthing conversations surrounding Chagas disease on YouTube, Cogent Social Sciences, 7:1, 1858561, https://doi.org/10.1080/23311886.2020.1858561
  • Khan, M. Laeeq (2020). Big Data and Entrepreneurship. In L. M. Mahoney & T. Tang (Eds.), Handbook of Media Management and Business (Volume 2, pp. 391-406). Rowman & Littlefield. ISBN-13 : 978-1538115305. 
  • Khan, M. L., Welser, H. T., Cisneros, C., Manatong, G., & Idris, I. K. (2020). Digital inequality in the Appalachian Ohio: Understanding how demographics, internet access, and skills can shape vital information use (VIU). Telematics and Informatics, 101380. https://doi.org/10.1016/j.tele.2020.101380
  • Khan, M. L., & Idris, I. K. (2019). Recognize Misinformation and Verify Before Sharing: A Reasoned Action and Information Literacy Perspective, Behavior & Information Technology, https://doi.org/10.1080/0144929X.2019.1578828. 
  • Welser, T., Khan, M. L., & Dickard, M., (2019). Digital remediation: social support and online learning communities can help offset rural digital inequality, Information, Communication & Society. https://doi.org/10.1080/1369118X.2019.1566485.
  • Khan, M. L., Zaher, Z., & Gao, B. (2018). Communicating on Twitter for Charity: Understanding the Wall of Kindness Initiative in Afghanistan, Iran, and Pakistan. International Journal of Communication, 12, 25. http://ijoc.org/index.php/ijoc/article/view/7726. 
  • Khan, M. L. (2017). Social Media Engagement: What Motivates User Participation and Consumption on YouTube? Computers in Human Behavior, 66, 236-247. https://doi.org/10.1016/j.chb.2016.09.024.

Conference Presentations & Panels

  • Khan, M. Laeeq, Government social media use during a pandemic: Best Practices, Panelist for Diskusi Online Kehumasan: Kepercayaan Publik, Media Digital, dan Transformasi Lembaga di Masa Pandemi December, 2020, Universitas Paramadina, Jakarta, Indonesia.
  • Khan, M. L. Social Media Management: Teaching Data Analytics, Social Media Marketing, and  Content Strategy (Teaching Panel), Sponsored by: Media Management, Economics, and Entrepreneurship Division and Communication Technology Division, Association for Education in Journalism and Mass Communication (AEJMC),  August, 2019, Toronto, Canada.
  • Grigoryan, N; Idris, I.; Gao, B.; King, R.; Khan, M. L.; and Titsworth, S., Navigating Fake News: An Assessment of Students’ New Media Literacy Skills” in News Division, Broadcast Education   Association (BEA), August, 2019, Las Vegas, NV. [2nd Best Paper Award]
  • Khan, M. L. (2018). Theoretical and Ethical Challenges and Strategies of Teaching Digital Analytics, Communication Technology and Newspaper and Online News Divisions, Association for  Education in Journalism and Mass Communication (AEJMC),  August 09, 2018, Washington D.C.
  • Khan, M. L. (2018). Social Media Analytics at Crossroads, Panel Research Presentation in Something Old, Something New, and Something Borrowed: A Discussion of Mass Communication Research Matrimony, Broadcast Education Association (BEA), April 07-10, 2018, Las Vegas, NV.
  • Khan, M. L. (2017). If You Build It, They Will Post”: The Beneficial Applications of Higher Education Institutions Creating and Utilizing Social Analytics Labs, Human Communication and Technology Division, National Communication Association (NCA), Nov 16-19, 2017, Dallas, TX.
  • Sweitzer, B; Khan, M. L. (2017). Dialogic Orientation of U.S. Universities on Twitter: A Social Media Analytics Approach, Panel: Studying Audience Behavior in a Convergent Environment: Locating Synthesis and Integration in Audience Research and Analytics, Broadcast Education Association (BEA), April 22-25, 2017, Las Vegas, NV, United States.
  • Khan, M. L, Zaher, Z.; Newton, G., (2017). How social media defined Rio Olympics: A text analytics approach towards understanding the impact of Zika Virus, International Communication Association (ICA) Conference, May 25-30, 2017, San Diego, CA, United States.
  • Khan, M. L, Zaher, Z. (2017). Sharing online to caring offline: How social media helped build Walls of Kindness across three countries, International Communication Association (ICA) Conference, May 25-30, 2017, San Diego, CA, United States.
  • Khan, M. L., (2015). It’s all about Relatedness: Social Media Engagement—A Self Determination Framework. Association for Education in Journalism and Mass Communication (AEJMC), August 06-09, 2015, San Francisco, C.A., United States.