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The increasing role of algorithms shaping our use of communication technology—particularly on social media—comes with a growth of empirical research attempting to assess how literate users are regarding these algorithms. This rapidly emerging field is marked by great diversity in terms of how it theorizes and measures our understanding of algorithms, due, in part, to the opaque "black box" nature of the algorithms themselves. In this review article, we summarize the state of knowledge on algorithmic literacy, including its definitions, development, measurement, and current theorizing on human–algorithm interaction. Drawing on this existing work, we propose an agenda including four different directions that future research could focus on: (1) balancing users' expectations of algorithmic literacy with developers' responsibility for algorithmic transparency, (2) methods for engaging users in increasing their literacy, (3) further developing the affective and behavioral facets of literacy, and (4) addressing the new algorithmic divide.

With social media platforms becoming primary news sources, concerns about credibility judgments and knowledge grow. This study ( N = 233) experimentally tests the effects of multiple source cues on Facebook news posts on credibility and knowledge. Judgments of story credibility were directly influenced by media source cues, but not friend source cues. Involvement in the source topic moderated the effects of these source cues, such that particular combinations influenced credibility differently, and also influenced cognitive elaboration about the topic. Theoretical implications for cognitive mediation model of learning from the news and the heuristic-systematic model of information processing are presented.

Social media play an important role in political communication, leading to growing concerns about the credibility of shared information. Attempts to slow the spread of misinformation by platforms such as Facebook and Twitter include adding fact-checking labels to social media posts, the effectiveness of which remains unclear. Using two experiments, we tested the credibility effects of fact-checking labels (confirmed vs. disputed) on graphical presentations of political (Democrat vs. Republican) quotes and social media news posts. Study 1 ( N = 312) tested the effects of these labels on political social media posts with political quotes, and Study 2 ( N = 356) replicated and extended this research to a news story post about a politician. Results indicate that the valence of verification labels on their own do not affect perceptions of the content. Instead, users find corrections of opposite-party political figures more credible and are more willing to share content when it negatively portrays opposite-party political figures. These results demonstrate potential limitations of fact-checking labels and highlight the importance of considering political ideology in correcting misinformation on social media sites.