1. Garrett, R. K., “Echo chambers online?: Politically motivated selective exposure among Internet news users”, (2009)
2. Resnick, Paul, “Bursting Your (Filter) Bubble: Strategies for Promoting Diverse Exposure”, (2013)
Summary:
The first paper is about the selective exposure of online news, whether a user’s consumption of online news based on his political background or not. The author conducted an experiment on 727 online users from two news websites (AlterNet and WorldNetDaily) with different political leaning (left and right respectively). The author tracked their usage and browsing behavior. Each user was given a set news articles about different political controversial topics. The results of this study suggest that opinion-reinforcing storied gets more exposure while opinion-challenging articles get less exposure. He also found that users do not avoid opinion-challenging news and spend some time reading them.
The second paper mentions different strategies developed to diminish selective exposure and promote diverse exposure of information among online users.
Reflection:
– I would prefer if the author of the first paper conducted a longitudinal study and later asked those users how the exposure to opposite point-of-view would challenge their beliefs and how far it might change it.
– I would like also to design a method that presents the counter-attitudinal opinion/news in an acceptable way for the users without triggering their beliefs’ self-defense and reject the information from the opposite side. May be this could be achieved by merging different strategies mentioned in the second paper.
– Those papers inspire me to build a database of profiles of news media (broadcast and online). I would collect data such as their political leaning, their stance towards popular and controversial topics, their credibility. I would also record their connectivity to each other and to real world entities (such as countries, governments, parties, businessmen … etc). I would also give each of them different metrics representing how much they broadcast misinformation (rumors and fake news). I believe such dataset would be very beneficial.
– I would like to measure how far the selective exposure of news articles on online news website different from the news that appear in the news feed of Facebook and twitter. I mean would the personalization of news feed on Facebook and twitter be similar to our selection on online news websites.
– I want also to study if people of similar political backgrounds are clustered together on Facebook and twitter (I mean from the network analysis view). Does me and my online friends (on Facebook) share similar beliefs and political preferences and how often we appear in each others news feed (our posts and comments).
– In my opinion the reading time metric is irrelevant and misleading. Since the reading time of each article depends on different factors such as the article length (longer articles need longer time to read), the vocabulary and language difficulty (which might the user reading speed) and also the education levels of the users (which will clearly affect their reading speed and information digestion).
