Arjun Wilkins
Biographic Data
| ID | 4796952 |
|---|---|
| NAME | Arjun Wilkins |
| GIVEN NAMES | Arjun |
| FAMILY NAME | Wilkins |
| SIGNATURE | WILKINS A |
| AFFILIATIONS | Stanford University |
| ORCID | 0009-0000-5315-356X |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 89 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 3 |
The Diffusion and Reach of (Mis)Information on Facebook during the U.S. 2020 Election
Social media creates the possibility for rapid, viral spread of content, but how many posts actually reach millions? And is misinformation special in how it propagates? We answer these questions by analyzing the virality of and exposure to information on Facebook during the U.S. 2020 presidential election. We examine the diffusion trees of the approximately 1 B posts that were re-shared at least once by U.S.-based adults from July 1, 2020, to Feb…
Like-minded sources on Facebook are prevalent but not polarizing
Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem 1,2 . Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ s…
Asymmetric ideological segregation in exposure to political news on Facebook
Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increase…
How do social media feed algorithms affect attitudes and behavior in an election campaign?
We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content the…
To Lag or Not to Lag
Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This ac…
Electoral Security of Members of the U.S. House, 1900–2006
Previous studies have documented that the increase in the incumbency advantage in the 1960s did not decrease the probability of defeat of incumbents in the U.S. House. I define a method for establishing bounds on the probability of incumbent defeat and find that it decreases significantly in the 1950s, before the rise of the incumbency advantage. Incumbency advantage does not have a direct relationship with incumbent defeat rates, raising questio…
The 2010 Elections
In President Obama's words, the Democratic Party experienced a “shellacking” in the 2010 elections. In particular, the net loss of 63 House seats was the biggest midterm loss suffered by a party since 1938—the largest in the lifetimes of approximately 93% of the American population
To Lag or Not to Lag
Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This ac…
The 2010 Elections
In President Obama's words, the Democratic Party experienced a “shellacking” in the 2010 elections. In particular, the net loss of 63 House seats was the biggest midterm loss suffered by a party since 1938—the largest in the lifetimes of approximately 93% of the American population
Electoral Security of Members of the U.S. House, 1900–2006
Previous studies have documented that the increase in the incumbency advantage in the 1960s did not decrease the probability of defeat of incumbents in the U.S. House. I define a method for establishing bounds on the probability of incumbent defeat and find that it decreases significantly in the 1950s, before the rise of the incumbency advantage. Incumbency advantage does not have a direct relationship with incumbent defeat rates, raising questio…
The 2010 Elections
In President Obama's words, the Democratic Party experienced a “shellacking” in the 2010 elections. In particular, the net loss of 63 House seats was the biggest midterm loss suffered by a party since 1938—the largest in the lifetimes of approximately 93% of the American population
Electoral Security of Members of the U.S. House, 1900–2006
Previous studies have documented that the increase in the incumbency advantage in the 1960s did not decrease the probability of defeat of incumbents in the U.S. House. I define a method for establishing bounds on the probability of incumbent defeat and find that it decreases significantly in the 1950s, before the rise of the incumbency advantage. Incumbency advantage does not have a direct relationship with incumbent defeat rates, raising questio…
To Lag or Not to Lag
Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This ac…
Like-minded sources on Facebook are prevalent but not polarizing
Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem 1,2 . Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ s…
Asymmetric ideological segregation in exposure to political news on Facebook
Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increase…
How do social media feed algorithms affect attitudes and behavior in an election campaign?
We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content the…
The Diffusion and Reach of (Mis)Information on Facebook during the U.S. 2020 Election
Social media creates the possibility for rapid, viral spread of content, but how many posts actually reach millions? And is misinformation special in how it propagates? We answer these questions by analyzing the virality of and exposure to information on Facebook during the U.S. 2020 presidential election. We examine the diffusion trees of the approximately 1 B posts that were re-shared at least once by U.S.-based adults from July 1, 2020, to Feb…
Political science (6 works) · Computer Science (5 works) · Internet privacy (4 works) · Politics (4 works) · Social Media and Politics (4 works) · Advertising (3 works) · Business (3 works) · Economics (3 works) · Ideology (3 works) · Law (3 works)