Literature Review Synthesis Matrix A synthesis matrix is one way to organize your sources. You can compare, contrast, and categorize your different sources by key concepts, themes, or main ideas. This visual representation of how your research relates will help you organize your sources logically and efficiently. Instructions: Complete this matrix by identifying your key concepts, and then write down a few bulleted notes on each source. Note the page numbers if you include direct quotes or paraphrases of specific ideas. A template is on the next page, followed by an example. In Microsoft Word, click anywhere in the table and then use the Layout tab in the Table Tools to insert additional rows and columns as needed. Template My research question: Methods Source 1: Citation Source 2: Citation Source 3: Citation Concept 1: Concept 2: Concept 3: Gaps, Problems, Unresolved Questions, Notes on Sources Example My research question: How can we use machine learning to analyze social media data related to HIV? Methods Source 1: Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PloS one, 6(5), e19467. Concept 1: Concept 2: Concept 3: Social Media Data HIV Machine Learning Collected and stored ● Able to make predictions a large sample of about swine flu using public tweets that social media data matched a set of ● This data is vital given pre-specified search that “an influenza terms and surveillance program geocoded. does not exist” (p. 3) Estimated rate of disease and public sentiment toward swine flu Gaps, Problems, Unresolved Questions, Notes on Sources “When and where tweets are less frequent (or where only a subset of tweets contain geographic information), the performance of our model may suffer.” Source 2: Chiu, C. J., Menacho, L., Fisher, C., & Young, S. D. (2015). Ethics issues in social media–based HIV prevention in low-and middle-income countries. Cambridge Quarterly of Healthcare Ethics, 24(3), 303-310. Source 3: Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8. Quantitative survey assessing participants’ perspectives on educational intervention ● Increasing social media ● Most participants use in low- and middlefelt like they income countries benefited ● Participant took part in positively from an HIV education the program and program on Facebook learned about HIV prevention ● All participants were MSM Collected public tweets and analyzed mood ● Gathered data from Twitter posts that explicitly states moods (e.g. “I’m feeling…”) ● Found that positive/negative sentiment on Twitter is 87.6% accurate for predicting stock market average ● Note: Helpful article for including diverse perspectives ● Used a “Self-Organizing Fuzzy Neural Network” to predict Dow Jones Industrial Average (p. 1) Modified from UCLA. Creative Commons Attribution-NonCommercial-ShareAlike by The WI+RE Team 2021

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