The article is considered a primary source, because while it references to several other sources for study, it generates primary information from this particular subject. The study focuses on social media misinformation during the COVID-19 pandemic, and is aimed at exploring its characteristics, determinants, impacts, theoretical perspectives, and strategies for halting its spread. Authors Muhamed and Mathew comprehensively explained how misinformation is propagated by several causes: anxiety which prevents such tension in efforts of filling in the missing information, source ambiguity due to trustworthiness to the origin of information, personal involvement, social ties, confirmation bias which reinforces preexisting beliefs, attractiveness of the platform, illiteracy, ease of sharing options, and device attachment. In terms of mitigation, the study suggests early communication to public officials and health professionals, use of scientific evidence for fact-checking, rumor refutation, intervention on misinformation by experts, all of which are aligned to the same goal. The study takes from 28 credible sources to discuss misinformation using a systematic literary review. Along with the resources used were tables and graphs that illustrate an overview of the results. While the discussion is rich in explanations and details, the study is obviously limited because of its research on specific domains.

This study is a secondary source as it compares data from existing researches. The authors compare several studies on disinformation targeted to develop a theoretical framework that discusses the prevalence and measurable conditions that are responsible for the spread of disinformation. Confirmation bias and motivated reasoning are two big factors in consumption and propagation. Other agents include social bots which spreads false political information resulting in misrepresented viewpoints, false information by strategic actors, and the phenomenon of naive realism that rejects views that are opposite of one's own. The accumulation of massive amounts of manipulated information combined with techniques and platforms all contribute to information pollution. Results on resiliency to disinformation between countries are explained through the number and values of framework indices. Results are categorized into three clusters - one country group with high resilience to disinformation has consensus political systems, strong welfare states, and pronounced democratic corporatism, and two groups with low resilience to disinformation – identified by its low levels of polarization, populist communication, social media news use, low trust levels, low shared media consumption, politicized, and fragmented environment. Frameworks, principal components factor analysis, and graph indices were used in the study to support and interpret the results.