Tuesday, January 09, 2018

New Paper: Cancer and Social Media

Continuing our work on geosocial analysis we recently had a paper entitled "Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram" published in the Journal of Health Communication. In the paper we  present a comparative study of differences in messaging for women’s and men’s cancer campaigns on social media through three discrete approaches. 
  1. we directly compare the incident rates of women’s and men’s cancers in the United States to the corresponding levels of traffic that these cancers elicited during World Cancer Day across two social media platforms, Twitter and Instagram. 
  2. we examine social media activity for breast cancer versus prostate cancer on both Twitter and Instagram during the dedicated month-long campaigns (October and November, respectively). 
  3. we compare the top terms associated with each campaign on these two social media platforms to discover whether there are differences in the terms associated with these online discussions.
Below you can read the abstract to our paper, see some of our results and at the bottom of the post have the full citation and link to the paper.

Abstract: 
Social media are often heralded as offering cancer campaigns new opportunities to reach the public. However, these campaigns may not be equally successful, depending on the nature of the campaign itself, the type of cancer being addressed, and the social media platform being examined. This study is the first to compare social media activity on Twitter and Instagram across three time periods: #WorldCancerDay in February, the annual month-long campaigns of National Breast Cancer Awareness Month (NBCAM) in October and Movember in November, and during the full year outside of these campaigns. Our results suggest that women’s reproductive cancers – especially breast cancer – tend to outperform men’s reproductive cancer – especially prostate cancer – across campaigns and social media platforms. Twitter overall generates substantially more activity than Instagram for both cancer campaigns, suggesting Instagram may be an untapped resource. However, the messaging for both campaigns tends to focus on awareness and support rather than on concrete actions and behaviors. We suggest health communication efforts need to focus on effective messaging and building engaged communities for cancer communication across social media platforms.


A comparison of percentages of cancer cases (green bars) and references to corresponding cancers in Twitter (blue bar) and Instagram (orange bar) during World Cancer Day 2016.

 References to breast cancer (green line), prostate cancer (orange line), and Movember (blue line) over the full year 2015 in Instagram.

Full Reference: 

Vraga, E., Stefanidis, A., Lamprianidis, G., Croitoru, A., Crooks, A.T. Delamater, P.L., Pfoser, D., Radzikowski, J. and Jacobsen, K.H. (2018), Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram, Journal of Health Communication. DOI: https://doi.org/10.1080/10810730.2017.1421730. (pdf)

Friday, December 29, 2017

Models from Teaching CSS

Most of the time when I teach a class instead of setting a final exam, I ask the students to carryout an end of semester research project. In my Introduction to Computational Social Science classes (both at the graduate and undergraduate level), this project entails the development of a computational model in an area of  interest to the student (or at the undergraduate level, students can opt to systematically explore someone else's model). The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.:
  • to understand the motivation for the use of computational models in social science theory and research;
  • to learn about the variety of CSS research programs across the social science disciplines;
  • to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.
Below you can see some of the outputs from these projects this last fall. The models range in type from agent-based models, cellular automata models to discrete event simulations (aka. queuing models) applied to a variety of topics from elephant poaching, artists and patrons, inheritance and wealth accumulation, the spread of religion, to that of looking at serving times at a Chipotle Mexican Grill.



 

Wednesday, December 13, 2017

Come Work with Us: 2 Postdocs in Urban Simulation

The George Mason University Department of Geography and Geoinformation Science within the College of Science, has an immediate opening for two postdoctoral fellows (up to 2-years), subject to budgetary approval. These positions will be part of the “Urban simulation” project team conducting research as part of the DARPA’s “Ground Truth” program, a network of DARPA-funded teams across the USA. The GMU team is directed by Andreas Z├╝fle, Dieter Pfoser, and Andrew Crooks and supported by Carola Wenk at Tulane University. George Mason University has a strong institutional commitment to the achievement of excellence and diversity among its faculty and staff, and strongly encourages candidates to apply who will enrich Mason’s academic and culturally inclusive environment.

Postdoc 1

Responsibilities:
The primary job responsibilities of this position consist of the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. A main goal is to create computationally efficient agent logic, thus allowing millions of agents to make decisions, find shortest paths between locations, and interact with their simulated world at the same time. For this purpose, implemented algorithms will need to be highly parallelizable, thus allowing to scale simulation via distribution among computing clusters located at GMU and Tulane. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:
  • Ph.D. in computer science, data science, or closely related field;
  • Strong programming skills in Java;
  • Excellent written communication skills demonstrated by prior publications;
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.

Preferred Qualifications:
  • Solid knowledge of graph algorithms;
  • Experience with Agent-Based Modeling and social science simulation;
  • Experience in design and implementation of software systems.

Postdoc 2

Responsibilities:
The primary job responsibilities of this position will be the design of an agent-based model based on the first principles underlying human needs, social interactions, and mobility to define socially plausible causalities. This model will contribute towards the design, development and refinement of an agent-based simulation framework for urban areas. Using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java), new agent logic will have to be implemented, thus creating agents that use socially plausible rules for mobility and interaction with other agents. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:
  • Ph.D. in computer science, data science, or closely related field; 
  • Experience with Agent-Based Modeling and social science simulation; 
  • Excellent written communication skills demonstrated by prior publications; 
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.

Preferred Qualifications:
  • Strong programming skills in Java;
More Information: https://jobs.gmu.edu/postings/42109

Monday, December 11, 2017

Come work with us

The  Department of Computational and Data Sciences is currently looking for 2 Assistant Professors starting in the Fall of 2018.

Assistant Professor, Statistics and Visualization (Tenure-Track)

Required Qualifications:
Applicants must hold a Ph.D. in a closely related field from an accredited institution, and should have research interests congruent with the associated research centers listed below. Applicants should have a promising research record with a deep knowledge of and interest in computation as applied to statistical analysis and visualization, with a strong emphasis in one or more of the following fields: machine learning, Bayesian inference, and knowledge mining. Candidates should have a demonstrated ability or strong potential to attract funding and provide strong contributions to the continued growth of the academic programs.

More Information: https://jobs.gmu.edu/postings/41997


Assistant Professor, Data Science (Tenure-Track )

Required Qualifications:
Applicants must hold a Ph.D. in data science from an accredited institution or an aptly related field such as computer science, computational science, a physical science, etc., and should have research interests congruent with the associated research centers listed below. Candidates should have a demonstrated ability or strong potential to attract funding and provide strong contributions to the continued growth of the academic programs. Applicants should have a promising research record and a demonstrated deep knowledge in the expanding field of data science, with a strong emphasis in one or more of the following fields: data mining, knowledge mining, big data analytics, data engineering, and image analysis.

More Information: https://jobs.gmu.edu/postings/42005

About the Department: CDS is a rapidly growing department offering an undergraduate degree in Computational and Data Sciences and graduate programs in CSI (Computational Sciences and Informatics) and CSS (Computational Social Science). Interdisciplinary research directions are in modeling, simulation, data science, and computational social science. The successful candidate will benefit from a highly collaborative research environment that supports a wide array of shared facilities and dynamic research centers, such as the DataLab, the university-wide Center for Social Complexity, and the Department’s Center for Simulation and Modeling.


Friday, November 17, 2017

New Paper: Social Media and Cancer Campaigns

Continuing our work on geosocial analysis we recently had a paper entitled "Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election" published in  World Medical and Health Policy. In the paper we show through the analysis of Twitter and Google Trends, how public engagement with breast cancer and prostate cancer awareness months between 2015 and 2016 changed. Specifically we found that attention to breast cancer and prostate cancer declined in 2016 (during the U.S. presidential election), when compared to 2015. Based on our finding we suggest that future cancer education campaigns—and campaigns for other health issues and policies—would benefit from monitoring the broader issues producing social media engagement, and adjusting their timing or communication strategies to ensure that public engagement with their key messages remains strong even in a crowded social media marketplace. Below you can read the abstract to our paper, see some of our key findings along with the full citation to the paper.

Abstract:
Cancer awareness campaigns compete with other health and social issues for public attention. We examined whether public engagement with breast cancer and prostate cancer declined in 2016 during the U.S. presidential election compared to 2015 on Twitter and Google Trends. We found that attention to breast cancer and prostate cancer declined in 2016 before the election as compared to 2015 in Twitter posts and Google searches. The findings suggest that cancer information seeking behavior, passive exposure to health communication, and active participation in social media about cancer all decreased during the peak weeks of the 2016 election season. Future health promotion initiatives and information dissemination efforts will benefit from monitoring the major issues garnering social media attention and then adjusting their timing or communication strategies to ensure that public engagement with their key policy messages remains strong when emerging news stories capture public interest.

Keywords: Twitter; breast cancer; prostate cancer

Twitter Traffic about Breast Cancer (a) and Prostate Cancer (b) in the Period October 2015–November 2016, and Comparison of October and November Traffic in 2015 and 2016 for Breast Cancer (c) and Prostate Cancer (d). All 2015 Data are Shown in Red and All 2016 Data are Shown in Blue.


Full Reference:
Vraga, E. K., Radzikowski, J., Stefanidis, A., Croitoru, A., Crooks, A.T., Delamater, P., Pfoser, D. and Jacobsen, K. H. (2017). Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election. World Medical and Health Policy, 9(4): 456–465. (pdf)