Paper #1: Echo Chambers Online?: Politically Motivated Selective Exposure among Internet News Users
Paper #2: Exposure to ideologically diverse news and opinion on Facebook
Summary #1:
This paper checks whether selective exposure plays any significant role in people’s choice of a news article in high-choice information environment like the Internet. The traditional approach of opinion reinforcement seeking and opinion challenge avoidance doesn’t make sense on the Internet. This paper suggests that this is due to people’s tendency to more focus on opinion reinforcement than filtering opinion challenging information. The study was run on 700+ people recruited online. Each of the participants is shown a set of articles related to a politically relevant topic and asked their interest in reading those articles in two phase. Thier reading time of the articles is also recorded. Based on the result it appears that people tend to select more stories with opinion reinforcement while spending more time on opinion challenging articles.
Summary #2:
This paper compares the influence of algorithm and user’s clickthrough in exposure to cross-cutting stories on Facebook. The network structure in Facebook is different than other social media due to connections in different offline context (school, family, social activity etc.). This is probably why homophily structure in Facebook is different. The authors use 10 million Facebook users who self-reported their political affiliation and their click activity on 7 million web URLs shared to analyze the influence. The analysis was limited to hard contents that have political significance. User’s news feed algorithm depends on various things on Facebook including how many times they visit the site daily, how much they interact with their friends, how often they click certain web URLs etc. The authors’ found that algorithmic exposure to cross-cutting content for conservative users was significantly higher than liberal users while the random probability of seeing cross-cutting content was the opposite. The click rate further limits their exposure.
Reflection #1:
This study was designed in a very simplistic way. The author has already noted that population was selected from two partisan sites which might have influenced the result. By selecting a different set of users it is easy to check if people with more partisan view are more likely to want to see the opposing view. Author’s inference of the non-existence of echo chamber is a bit of a stretch. One thing interesting in the result was that people had more interest in “gay marriage” than “civil liberty” while their reading time on “gay marriage” is less than that of “civil liberty”. This conflicts with the common notion that people spend more time things of interest. People seem to find most stories more opinion challenging than opinion reinforcing. The variance in this opinion seems to increase when people have read the stories. Another variation of this could be checking how much difference it makes in case of just headlines of a story is presented to users. The study didn’t ask if the users already read a story. This could make a significant difference given that news aggregator tend to show most popular stories first. This could also account for less time people spend on stories of interest. One thing I didn’t understand was why use log of the time to fit a linear regression model as the time span was in the range of 4sec – 9min. Another particular odd inference was author thought the correlation of reading time with Age was not significant with coefficient .01 for p < .01 while it was significant in case of opinion reinforcement (retrospective) with coefficient .02 with p < .05.
Reflection #2:
This paper checks what is the role of users click through in the limiting exposure to diverse content. Although the user selection process had a self-reporting bias, the proportion of liberal and conservative users were very close surprisingly. Figure 3 has several implications given certain condition is met. They failed to mention the number of average shares by both conservative and liberal users. Although they mentioned the total proportion of different types of shares by both conservative and liberal users, the average number of shares could skew the result. Similar relation could be found in the proportion of liberal and conservative contents in the 226,000 hard contents. They also didn’t mention users’ living area (city or rural) which could skew the percentage of ties. The authors did find a correlation between the position of a story in the news feed and click through ratio which might have affected the inference. They used a technique to calculate the risk ratio (results not shown in the supplementary text) but provided a partial method for calculation.
