Contemporary Issues in Communication & Media Theory

This assignment invites you to test some of the ideas contained in Acker & Donovan’s “Data Craft: A
theory/methods toolkit for critical internet studies.” — which is uploaded to the files. As they argue, researchers interested in how digital platforms mediate public discourse face a number of challenges in accessing “usable” datasets. These have to do primarily with the proprietary and “black boxed” nature of platform APIs (Application Programming Interface).

Your task is to employ some of Acker and Donovan’s suggestions for overcoming these research barriers.

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You will search through social platforms such as Twitter, Facebook, or Instagram on the hunt for
misinformation. The goal is to find and gather evidence of recent “data craft” campaigns in which groups
or individual users have attempted to manipulate social media ecosystems by spreading information
that is misleading, untrue, or “fake news.” Examples of what you are looking for can be found in Acker &
Donovan’s discussion of the Russian Internet Research Agency, Zadrozny and Collins’s discussion of The Epoch Times, and ReplyAll’s investigation of QAnon. Be sure to engage each of these closely.
Once you have found evidence you suspect might be part of a coordinated misinformation campaign,
use Acker & Donovan’s “step by step rubric” (pp. 9-15) for working with publicly available metadata
(timestamps, usernames, content tags, follower lists, etc.). They suggest some ways we can gather and
analyze such metadata in order to draw conclusions about how disinformation campaigns are
coordinated to spread misinformation about a particular issue, person, or event.

You will submit (1) a 1500-word discussion paper of your findings, as well as (2) a “Research Appendix”
that contains images, screenshots, and any other material related to your data set (such as Acker &
Donovan include in their paper). Your discussion paper should summarize your experiences in gathering
and working with such data. You should also develop an argument – even if provisional – about what
your data set seems to indicate or be evidence of.

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