Upon installation and baseline survey, each user has a five-day pre-exposure period. During the pre-exposure period, a randomly selected article from a randomly selected news source about a randomly selected issue is provided each day. Pre-exposure to randomly chosen news sources during this period serves as an important exogenous variation for the test of mechanisms. This period also serves as a grace period, alleviating the attrition problem by screening out those who were going to drop out early.
After pre-exposure period, each user reads an article about a randomly selected issue each day. For analysis, I include one article reading per issue per user after pre-exposure period, taking into account the rapid dropout of users.
I assign the users who finish the pre-exposure period into one of seven treatment groups. The everyday experience of the treatment groups is slightly different. First—a common step for every treatment group—the issue of the day for each user is randomly selected by the app. The first three groups are allowed to select the news source from which to read an article about the issue of the day. The remaining four groups are given randomly selected articles. When making source selections (G1-G3), the first two groups (G1-G2) can see the names of the news sources while group G3 cannot. The average positions of the sources on the issue at hand are shown only to G2 and G3, and not to G1. For those who are not allowed to select the news source (G4-G7), there is a screen, immediately before reading the randomly chosen article, showing relevant information about the news source that published the article. The information about the news source shown to the readers varies (name only, name and position, position only, nothing). Regardless of the treatment status, everyone can determine the news source at the time of reading the article with a minimal effort because the articles always indicate the source’s name at the end of the text. Most articles also indicate the source’s name at the beginning and in the middle of the text.
Note: unfortunately, the average positions of the sources—shown in the app for G2, G3, G5, and G6—had coding errors unnoticed by the researchers until the very end of the research period: they almost always indicated source positions very close to the center of the scale. Anecdotally, users mostly ignored this scale due to this problem. Therefore, the experiences of G1 and G2 were roughly similar, as were the experiences of G4 and G5, and G6 and G7. Furthermore, the names of the news sources were always salient in the reading screen, making the experience of G4-G7 highly indistinguishable.
Given this, I aggregate the groups into three larger groups to maximize the statistical power, taking into account the similar experiences of the finer subgroups within these larger groups. The Source-Name Group includes G1 and G2—they are allowed to select their news source based on source names. The Source-Position group consists of only G3, which cannot see the names (thus it is harder for them to identify the media that were likely to advocate the supported party’s view) but can see source positions and select based on this information. The remaining groups (G4-G7) are not allowed to choose news sources and were given randomly selected articles. They are merged together as the No-Choice Group. All group comparisons in the paper are based on three larger groups.