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Society's reliance on social media as a primary source of news has spawned arenewed focus on the spread of misinformation. In this work, we identify thedifferences in how social media accounts identified as bots react to newssources of varying credibility, regardless of the veracity of the content thosesources have shared. We analyze bot and human responses annotated using afine-grained model that labels responses as being an answer, appreciation,agreement, disagreement, an elaboration, humor, or a negative reaction. Wepresent key findings of our analysis into the prevalence of bots, the varietyand speed of bot and human reactions, and the disparity in authorship ofreaction tweets between these two sub-populations. We observe that bots areresponsible for 9-15% of the reactions to sources of any given type butcomprise only 7-10% of accounts responsible for reaction-tweets; trusted newssources have the highest proportion of humans who reacted; bots respond withsignificantly shorter delays than humans when posting answer-reactions inresponse to sources identified as propaganda. Finally, we report significantlydifferent inequality levels in reaction rates for accounts identified as botsvs not.

How Humans versus Bots React to Deceptive and Trusted News Sources: A Case Study of Active Users