Model of Endorsement

Suppose n users choose whether to like m candidates. Each user i = 1, . . ., n chooses whether to like candidate j = 1, . . ., m by comparing the candidate’s position at ${\bm \zeta\ }_j$ and the status quo (in this case, not publicly supporting the candidate) located at ψ _j , both in R ^d , where d = dimensions of ideology space. Let

(1) \begin{eqnarray} y_{ij} = \left\lbrace \begin{array}{ll}1 & \hbox{if user $i$ likes candidate $j$}, \\ 0 & \hbox{otherwise.} \end{array} \right. \end{eqnarray}

User i receives utility for supporting candidates close to her own ideal point x _i in R ^d policy space. We can specify this ideological utility as a quadratic loss function that depends on the location of the candidate and the status quo:

(2) \begin{eqnarray} U_{ij}^{candidate} &=& -\Vert \bm {x}_i - \bm {\zeta\ }_j\Vert ^2, \nonumber \\ U_{ij}^{status\ quo} &=& -\Vert \bm {x}_i - \bm {\psi\ }_j\Vert ^2. \end{eqnarray}

The net benefit of liking is then the difference in these two utilities,

(3) \begin{eqnarray} U_{ij}^{like} &=& U_{ij}^{candidate} - U_{ij}^{status\ quo} \nonumber \\ &=& -\Vert \bm {x}_i - \bm {\zeta\ }_j\Vert ^2 + \Vert \bm {x}_i - \bm {\psi\ }_j\Vert ^2. \end{eqnarray}

Notice that the utility of liking is decreasing in distance between the candidate and the user, but increasing in the distance between the status quo and the user.

Finally, suppose also that the utility of liking is increased by candidate-specific factors ϕ _j that govern how desirable each political page is (some pages are more popular and, perhaps, easier to find on the site) and user-specific factors η _i that govern each user’s propensity to support candidates (some users get greater utility from the act of liking, and are thus more likely to engage in the activity than others). Putting these together with liking utility yields

(4) \begin{eqnarray} U_{ij} = -\Vert \bm {x}_i - \bm {\zeta\ }_j\Vert ^2 + \Vert \bm {x}_i - \bm {\psi\ }_j\Vert ^2 + \eta _i + \phi _j. \end{eqnarray}

To group row and column terms into new variables, we let β _j = ψ _j − ζ _j , and θ _j = ψ ² _j − ζ ² _j + ϕ _j . Thus (4) simplifies to

(5) \begin{eqnarray} U_{ij} = -2\bm {x}_i \bm {\beta }_j + \eta _i + \theta _j. \end{eqnarray}

We do not observe direct utilities, but we do observe likes. Suppose that observing a like means that the true utility of endorsing is high, while not observing one means that the true utility is low (without loss of generality, suppose the utilities are 1 and 0, respectively). Not all likes yield exactly the same utility, so we can think of the true utility as being equal to a function of the observed like (y_ij ) minus an error term (ν _ij ):

(6) \begin{eqnarray} U_{ij} = y_{ij} - \nu _{ij} \end{eqnarray}

Substituting, we get

(7) \begin{equation} y_{ij} = -2\bm {x}_i \bm {\beta }_j + \eta _i + \theta _j + \nu _{ij}. \end{equation}

To further simplify the model, we factor out the the η and θ terms by employing the double-center operator D(.) defined for a matrix Z to be each element minus its row and column means plus its grand mean divided by − 2:

(8) \begin{equation} D(z_{ij}) = (z_{ij} - \bar{z}_{i.} - \bar{z}_{.j} + \bar{z}_{..})/(-2). \end{equation}

In the literature that utilizes roll-call votes to estimate ideology, Poole (Reference Poole2005) and Clinton, Jackman, and Rivers (Reference Clinton, Jackman and Rivers2004) discuss the use of the double-center operator on a squared distance matrix, not on the roll-call matrix itself. The effect of this operator is to generate a new matrix with all row and column means equal to zero. As a result, any term that does not interact with both a row and column variable will factor out of the matrix.

Suppose ν _ij is an independent and identically distributed random variable drawn from a stable density. Suppose further, without loss of generality, that the dimension-by-dimension means of x and β equal 0. If so, then applying the double-center operator in equation (8) to both sides of equation (7) yields

(9) \begin{eqnarray} D(y_{ij}) = \bm {x}_i \bm {\beta }_j + \epsilon _{ij}, \end{eqnarray}

where the new error term ε _ij is also a stable density defining the stochastic component of the identity. We can now use singular value decomposition (SVD) of the double-center matrix of likes to find the best d dimensional approximation of x _i and β _j (Eckart and Young Reference Eckart and Young1936):

(10) \begin{eqnarray} D(\bm {Y}) = \bm {X} \bm {\Sigma } \bm {B}, \end{eqnarray}

where Y is the observed matrix of likes, X is an n × n matrix of user ideology locations, Σ is a n × m matrix with a diagonal of singular values, and B is a m × m matrix of βs. The d largest singular values correspond to the d columns of X and d rows of B that generate the best fitting estimates of x _i and β _j (Eckart and Young Reference Eckart and Young1936).

Although it is possible to analyze the full matrix of likes from users to political pages, the candidate (ϕ _j ) and user (η _i ) specific factors mentioned above bias the estimation. Because some pages are so much more popular than others, and to a lesser extent because some users like many more politicians than average, the estimation yields ideological estimates that are weighted by the relative popularity of the candidates. For instance, Obama is to the extreme left of the distribution and Romney and Palin are to the extreme right, while candidates who have few likes are in the middle of the distribution regardless of their ideological views.

To account for political page- and user-specific factors, we compute a political page adjacency matrix, A = XX′, in which the rows and columns correspond to political pages and entries consist of the number of times an individual user likes both pages, and then compute the ratio of common users, corresponding to an agreement matrix, G = a_ij/diag(a_i ), described in further detail below. We then apply SVD to the G matrix to estimate x _i .

Estimation of Ideology from Endorsements

We begin by creating a matrix in which each column represents a user and each row represents an official Facebook page about politics. We limit our data to Facebook users in the United States who are over age 18 who endorse, or “like,” at least two political pages.Footnote ⁵ This leaves us with approximately 6.2 million users and 1,223 pages and 18 million like actions from users to pages. An example of the first ten rows and first ten columns of the bipartite matrix is in Table 1.

TABLE 1. The First Ten Rows of the User by Political Page Matrix

Note: Entries in the matrix are dichotomous, where 1 means that the user has liked the page and . means that the user has not.

A few things should be clear from this example and the summary statistics described here. First, there are few likes relative to the size of the matrix overall, making the matrix sparse. Second, users vary in the number of candidates they support. Although we limit the data to include users who like at a minimum two candidates’ pages, the average number of likes is 3.04 and the maximum number of pages liked is 625. Figure 1 shows the full distribution of the number of likes for users and the number of fans per page. Most users like only a few pages, but a few like many. Similarly, political pages vary in the amount of support from users they attract. The maximum number of fans of a page is 3.67 million (Barack Obama), with an average of 15,422.5 fans per page. Most pages have a few thousand fans.Footnote ⁶

FIGURE 1. The Left Panel Shows the Distribution of the Number of Pages that Each User Likes; the Right Panel Shows the Distribution of the Number of Fans that Each Page Has

To estimate ideology our approach is similar to the approach used by Aleman et al. (Reference Aleman, Calvo, Jones and Kaplan2009) to estimate the ideal points of legislators in the United States and Argentina using cosponsorship data, and is similar in principle to the use of Relational Class Analysis by Goldberg (Reference Goldberg2011) and Baldassarri and Goldberg (Reference Baldassarri and GoldbergForthcoming). Estimating separate parameters for users and pages, as is typical of estimation techniques like DW-NOMINATE or Bayesian analysis (Clinton, Jackman, and Rivers Reference Clinton, Jackman and Rivers2004), would be difficult on a large, sparse matrix. Instead, we construct an affiliation matrix between the political pages in which each cell indicates the number of users that like both pages. We do not use the original (two-mode) dataset of connections between users and political figures, which is organized as an X = r × c matrix, with r = 1, 2. . .R users and c = 1, 2. . .C pages, but instead use an affiliation matrix (Table 2), A = XX′. In this affiliation matrix, the diagonal entries are the total number of users that like each page and the off-diagonal entries are the number of times an individual user likes both pages. Table 2 shows the first ten rows and ten columns of the affiliation matrix, A. The table shows that there are very significant differences in the total number of users that like each page, as well as notable differences in the number of users that like each pair of pages.

TABLE 2. The First Ten Rows of the Affiliation Matrix

Note: Diagonal entries are the number of fans of the page. Off-diagonal entries are the number of fans of both pages.

Next, we calculate the ratio of shared users by dividing the number of users that like both pages by the total number of users that like each page independently, which produces an agreement matrix, G = a_ij/diag(a_i ), as depicted in Table 3. Because each page has a different number of fans, the denominator changes and the upper and lower triangles of the new square matrix, G, are not identical. For instance, Barack Obama has many fans on Facebook (the most of any page in the data set) so the values in the first row are small as they are all divided by the number of fans that Obama has. However, many of the fans of other candidates also like Obama, so those values are relatively high.

TABLE 3. The First Ten Rows of the Ratio of Affiliation Matrix

Note: Because each page has a different number of fans, the denominator changes and the off diagonal entries are not identical.

It is notable that there is overlap in fans across partisan lines. Take, for instance, the Barack Obama column in Table 3: this column represents the proportion of the other politicians’ fans who are also Obama’s fans. Although it is not surprising that the other Democratic politicians have fans that are also Obama’s fans, more than 7% of both Romney’s and Palin’s fans are also fans of Obama. Thus, for Facebook users who are fans of at least two candidates, there is not complete polarization among Facebook users. This may owe to centrist users who are interested in following members of both parties or to users who are interested enough in politics to follow a large set of popular politicians regardless of their ideological affiliation.

The agreement matrix provides the information required for estimation. From this stage, a number of methods to scale the data may be employed. For simplicity, we use SVD on the centered matrix, G. Because we normalize the agreement matrix, which makes it asymmetric, the results from the left and right singular vectors are not similar. The right singular value is still highly related to the page’s popularity, as its denominator is the number of fans of the page. The left singular value is unrelated to popularity, as its denominator is unrelated to the popularity of the page. Therefore, we retrieved the first rotated left singular value as the ideology measure for the pages. We rescaled the values to have mean 0 and standard deviation 1 for ease of interpretation.

We were next interested in estimating ideology scores for the users. If we were to scale the entire user by page matrix we would be able to estimate separate parameters for the users and the pages that should be measures of ideology. Another approach would be to replicate what we have done with the pages and to create a matrix of connections between users based on shared political pages that they both like. This would create a matrix of approximately 6.2 M² entries. However, decomposing a 6.2-M × 6.2-M matrix generally requires more computational resources than are available to political scientists running R on a normal computer. Instead, for each user we take the average of the scores for the political pages that user endorsed.

We begin our exploration of our estimates by examining the distributions of the ideology scores of politicians and individuals, as shown in Figure 2. The figure shows that both individuals and politicians are bimodally distributed, which is similar to Poole and Rosenthal’s (Reference Poole and Rosenthal1997) results for the U.S. Congress, and Bonica’s (Reference Bonica2013) results for candidates for office. The bimodal distribution of individuals is consistent with a polarized American public (Abramowitz Reference Abramowitz2010; Abramowitz and Saunders Reference Abramowitz and Saunders2008; Levendusky Reference Levendusky2009), in contrast to those who have argued that polarization in the electorate has not increased (Dimaggio, Evans, and Bryson Reference Dimaggio, Evans and Bryson1996; Fiorina, Abrams, and Pope Reference Fiorina, Abrams and Pope2006; McCarty, Poole, and Rosenthal Reference McCarty, Poole and Rosenthal2006). However, our sample is not fully representative of the population. Indeed, because those in our sample are likely to be more politically engaged than average, our data may reflect that more politically engaged citizens are also more polarized (Abramowitz and Jacobson Reference Abramowitz and Jacobson2006; Fiorina and Levendusky Reference Fiorina and Levendusky2006). Finally, while the distributions of both sets of actors show evidence of polarization, politicians are more dispersed than individuals.

FIGURE 2. Density Plots of Ideological Estimates of 1,223 Politicians and 6.2 million Individuals.

Validation of Measures for Political Elites

To examine the similarity between legislators’ positions as derived from roll-call vote data and those from Facebook liking data, we matched Facebook pages to their corresponding DW-NOMINATE first dimension scores. We matched 465 pages to members of the 111th Congress. The Pearson correlation between the two measures is 0.94, and the within-party correlation for Democrats is 0.47 and for Republicans is 0.42. This correlation is quite high given that Aleman et al. (Reference Aleman, Calvo, Jones and Kaplan2009) find that the ideological estimates from roll-call data and ideological estimates from cosponsorship data in the U.S. Congress correlate between 0.85 and 0.94. Figure 3 provides a visual representation of the relationship between the two ideology scores. The figure shows that the measures cluster legislators into two parties, and that the correlation within parties is quite high as well. For comparison, Bonica (Reference Bonica2013) finds that the overall correlation between DW-NOMINATE and CFscores scores among incumbents is 0.89, with a Democratic correlation of 0.62 and a Republican correlation of 0.53.

FIGURE 3. Scatter Plot Showing the Relationship between the Facebook Based Ideology Measure and DW-NOMINATE

We have labeled some of the members of Congress in the figure to illustrate where some of the most extreme members and some of the more moderate members lie on both measures. There are two points for Jeff Flake and Ron Paul in Figure 3. Although most of the political figures in the dataset had only one page, some had more than one. Ron Paul maintained two official pages, one for his presidential candidacy and one for his work in Congress (this page explicitly asked for all discussion related to his presidential candidacy to move to the other page). Ethics rules state that members need to separate official business and campaign activity, and these separate pages may reflect an effort to comply. While it is unfortunate to have multiple pages representing the same individual with respect to reliably estimating ideology, it does allows us to see whether we get consistent ideological estimates across multiple pages. The similarity in ideological estimates for Flake and Paul’s pages suggests that our measurement strategy is reliable.

Validation of Measures for Ordinary Citizens

We next turn to the validation of the individual-level ideological estimates. First, we computed the average ideology score of individuals based on their stated political views. On Facebook users may fill out a free response field that many users fill out called “political views.” Many users type the same things in, such as “Democrat,” “Republican,” “Liberal,” or “Conservative.” We took all labels that more than 20,000 users had used and calculated the average ideology score for the group, as well as the 95% confidence interval for that estimate. The results are shown in Figure 4.

Notes: The category labeled “none” is the group of users that actually wrote the word “none” as their political views. The point labeled “(blank)” is the group of users that has not entered anything in as their political views. The 95% confidence intervals for each of the estimates is smaller than the point. The color of the points is on a scale from blue to red that is proportional to each group’s average ideology score.

FIGURE 4. Average Facebook Ideology Score of Users Grouped by the Users’ Stated Political Views

Figure 4 shows that the ideology score predicts users’ stated political views well. There appear to be at least three clear groups—those who state their political views and are liberal, those who do not state a clearly liberal or conservative ideology, and those who state their ideology and are conservative. There is also substantial variation in the middle group, those that do not state a liberal or conservative ideology. The groups represented are in approximately the order one would expect based on their average ideology, save for the fact that those who self-identify as “very conservative” are slightly to the left of those who self-identify as “conservative.”

While the above analysis suggests that the estimates we make for users are valid, stated political views on Facebook is a measure of political orientation that has not been previously analyzed. Stated political views of an individual on Facebook are difficult to interpret because we do not know how users understand the question. Therefore, we conducted a survey using a more standard ideology survey question to further understand if the estimates correlate with this commonly used ideology metric.

We use survey data from 20,027 individuals for whom we were also able to estimate ideology to further validate our ideology measure. These individuals consist of the intersection between individuals who took the survey and those who liked two or more pages. The survey was issued to a convenience sample on September 6, 2012 to U.S. Facebook users who were logged in. 282 thousand individuals started the survey and 78 thousand completed it; the approximate AAPOR response rate was 2.6 percent. Respondents’ demographics reflect a departure from U.S. Census demographics with respect to age (54% 18–34, 34% 35–54, and 11% 55+); gender (57% female); ethnicity (83% white); education (19% high school or less; 41% some college; 28% college graduate; 12% postgraduate); and, to a lesser extent, ideology (12% very liberal; 22% liberal; 42% moderate; 17% conservative; 6% very conservative).

We asked individuals to place themselves on a five point ideological scale and also for their party identification as either a Democrat, Republican, Independent, or Other. We then computed the average Facebook ideology score for each group. The results are shown in Figure 5.

Note: The 95% confidence intervals for each of the estimates is smaller than the point.

FIGURE 5. Average Facebook Ideology Score of Users Grouped by the Users’ Ideology (left panel) and Party Identification (right panel) from a Survey Conducted Through the Facebook Website

Figure 5 shows that the Facebook ideology score is strongly associated with traditional survey ideology and partisanship measures. The results show there are three clear groups: Liberals, Moderates, and Conservatives. Although the measure can statistically differentiate between those who answered “Liberal” from those who answered “Very liberal” and those who answered “Conservative” from those who answered “Very conservative,” the differences between those groups is small. Further, the ideology measure from Facebook is a good predictor of partisanship, with Democrats occupying the scale’s left end, Republicans the right, and Independents the middle. We do not expect the respondents to our survey to be representative of the population overall or of the population for which we measure ideology, but we also do not expect that either method will show bias in measurement for either population. Therefore, we use these data to validate the Facebook measure, not to make arguments about the overall population.

Example 1: Age and Ideology

A substantial literature has found a relationship between age and political ideology (Cornelis et al. Reference Cornelis, Hiel, Roets and Kossowska2009; Glenn Reference Glenn1974; Krosnick and Alwin Reference Krosnick and Alwin1989; Ray Reference Ray1985). Most studies have found that as people age they become more conservative and less susceptible to attitude change (Jennings and Niemi Reference Jennings and Niemi1978; Krosnick and Alwin Reference Krosnick and Alwin1989). However, less is known about how that relationship varies across individual characteristics. Recent studies have begun to investigate how personality plays a role in mediating the relationship between age and conservatism (Cornelis et al. Reference Cornelis, Hiel, Roets and Kossowska2009). Here we investigate how the relationship between age and ideology varies by characteristics such as gender, marital status, and educational attainment.

A key advantage of using the Facebook ideology data is the large number of observations. By looking at patterns in the raw data we can understand phenomena that are not as easily understood using standard techniques, such as regression. In order to study ideology across characteristics, we took all individuals for whom we calculated an ideology score and matched the individual’s characteristics that they listed on their profile. Figure 6 shows the average ideology of users age 18 through 80, then separates the estimates by gender, marital status, and college attendance. The figure shows that older people are more conservative than younger people. Women are more liberal than men but a similar pattern in which older women and older men are more conservative than their younger counterparts emerges. While the overall pattern is similar for those who get married and those who do not, young people who get married are more conservative than their younger counterparts and young people who do not get married are more liberal than their younger counterparts. After the age of approximately 35, the pattern of increasing conservatism with age is similar across the groups. Finally, college attendance is not predictive of ideology among the young, but for among older groups having attended college is related to being more liberal.

Notes: The upper left panel shows the average ideology of all users in our sample. The upper right panel shows the average ideology of men and women by age. The lower left panel shows the average ideology of married and unmarried individuals by age. The lower right panel shows the average ideology of college attendees and those who have not attended college by age.

FIGURE 6. In Each Panel the Points Show the Average Ideology of the Age Group for Individuals Age 18 through 80, and the Lines Represent the 95% Confidence Interval of the Estimate

While these results are consistent with previous research, we have not studied change in ideology over time. The finding that older people are more conservative than younger people is consistent with a population that becomes more conservative over time. However, it is also consistent with recent surveys that have shown that the most recent generation is among the most liberal in recent memory (Kohut et al. Reference Kohut, Parker, Keeter, Doherty and Dimock2007). While we have some evidence of how an individual’s ideology changes over time, we find that the correlation in ideology from March 2011 to March 2012 is 0.99. With more time we should be able to better discern whether the patterns we found are due to cohort effects or change in an individual’s ideology over time.

Example 2: Ideology in Social Networks

Another advantage of using the Facebook ideology data is the abundance of data about the social networks of its users. Previous work has shown that ideology clusters in social networks (Huckfeldt, Johnson, and Sprague Reference Huckfeldt, Johnson and Sprague2004). Here, we wish to characterize the extent to which the clustering varies based on the strength of the relationship between two individuals. We consider three general types of relationships: friendships, family ties, and romantic partners.

Clustering in the network may be due to some combination of three possibilities. First, clustering may be due to exposure to a shared environment. Friends may be both exposed to some external factor that influences both to change their ideology in a similar way (e.g., attending the same college (Newcomb Reference Newcomb1943)). Second, clustering may be due to homophily. That is, people may choose friends based on ideology (Heider Reference Heider1944; Festinger Reference Festinger1957). Third, clustering may be due to influence. That is, one friend may argue in favor of an ideological position and change their friend’s views (Lazer et al. Reference Lazer, Rubineau, Chetkovich, Katz and Neblo2010). These processes are more likely to occur between close friends than socially distant ones—closer friends are more likely to be physically proximate and hence more likely to experience the same external stimuli. Friends who share an ideology are more likely to become close friends as they will have more in common. Finally, close friends will have more opportunities to influence one another’s ideological views, which should also increase ideological similarity.

As social relationships become stronger, friends should be increasingly likely to hold similar ideological views. And, this pattern should be strongest for the most intimate relationships. Recent work has shown that while people do not usually select romantic partners based on ideology, they do base such decisions on factors related to ideology, which leads to highly correlated ideological views (Klofstad, McDermott, and Hatemi Reference Klofstad, McDermott and Hatemi2013). Furthermore, this pattern should grow stronger over time as partners are exposed to the same factors, influence each other, and as more similar partners should be more likely to stay together.

Similarly, we expect that familial relationships will show evidence of these processes. Members of a nuclear family are more likely to experience the same external stimuli, due to being more likely to live near one another. While parents cannot select their children (or vice versa) based on ideological views (or other characteristics related to it), recent work shows a genetic component to ideology (Hatemi et al. Reference Hatemi, Gillespie, Eaves, Maher, Webb, Heath, Medland, Smyth, Beeby, Gordon, Mongomery, Zhu, Byrne and Martin2011). This genetic component coupled with socialization and similar exposure to factors that influence ideology mean that ideology is likely to be correlated within family units.

Since Facebook allows users to identify their familial and romantic relationships, we were able to test for ideological similarity across these social links. We began by pairing all individuals with their siblings, parents, or romantic partners. We then calculated the Pearson correlation for each group. The results are shown in Figure 7. The figure shows that married couples have the highest correlation in ideology, while engaged couples have the second highest value. Correlations within the nuclear family have lower values, with parent-child relationships being stronger than sibling relationships. The fact that the correlation between siblings is lower than the correlation between parent-child pairs may be due to the fact that our sample skews toward younger users and that the set of parents on Facebook are more likely to be similar to their children across a range of factors than parents who have not yet joined the website.

FIGURE 7. The Correlation in Ideology for Familial and Romantic Relationships. The 95% Confidence Intervals for Each of the Estimates is Smaller than the Point

Next, we were interested in the correlation of ideology between friends. First, we paired all 6.2 million users for whom we estimated ideology in 2012 with every Facebook friend for which we also had an estimate for ideology, for a total of 327 million friendship dyads and an average of 53 friends per user. The overall correlation in 2012 was 0.69, which approximates other measures of ideological correlation among friends (Huckfeldt, Johnson, and Sprague Reference Huckfeldt, Johnson and Sprague2004). We repeated this procedure for 2011, with 6.1 million users who had 238 million friendship connections to other users for whom we estimate ideology.Footnote ⁷ The overall correlation between ideology among these friends in 2011 was 0.67. The slight increase in correlation of friends’ ideology from 2011 to 2012 is suggestive that there is greater polarization among friends in 2012.

Additionally, we categorized all friendships in each year of our sample by decile, ranking them from lowest to highest percent of interactions. Each decile is a separate sample of friendship dyads. We validated this measure of tie strength with a survey (see Jones, Settle, Bond, Fariss, Marlow, and Fowler (Reference Jones, Settle, Bond, Fariss, Marlow and Fowler2012) for more detail) in which we asked Facebook users to identify their closest friends (either 1, 3, 5, or 10). We then measured the percentile of interaction between friends in the same way and predicted survey response based on interaction between Facebook friends. The results show that as the decile of interaction increases the probability that a friendship is the user’s closest friend increases. This finding is consistent with the hypothesis that the closer a social tie between two people, the more frequently they will interact, regardless of medium. In this case, frequency of Facebook interaction is a good predictor of being named a close friend.

Using the decile measure of tie strength, we then calculated the correlation between user and friend ideology on each set of dyads for both 2011 and 2012 (see Figure 8). For both years the correlation in friends’ ideology increases as tie strength increases. The proportion of interaction between friends is a better predictor of similarity of ideology between friends in 2012 than in 2011, again suggesting that in 2012 friendships are more politically polarized than in 2011. We caution that this correlation may increase for other reasons, such as better measures of ideology in 2012 owing to users liking more political pages, or perhaps to changes of the makeup of the set of individuals for whom we can estimate ideology. Although we can only estimate ideology for about 2% more people in 2012 than 2011, the change in the sample could be enough to account for the difference in 2012.

Notes: Each decile represents a separate set of friendship dyads. Decile of interaction is based on the proportion of interaction between the pair during the three months before ideology was scaled. The 95% confidence intervals for each of the estimates is smaller than the point.

FIGURE 8. The Correlation in Ideology for Friendship Relationships

Example 3: Friend Ideology and Turnout

While the composition of ideology in social networks is important on its own, considerable research has been applied to understanding how the makeup of an individual’s social network affects political participation (Eveland and Hively Reference Eveland and Hively2009; Huckfeldt, Johnson, and Sprague Reference Huckfeldt, Johnson and Sprague2004; Huckfeldt, Mendez, and Osborn Reference Huckfeldt, Mendez and Osborn2004; McClurg Reference McClurg2006; Mutz Reference Mutz2002; Scheufele et al. Reference Scheufele, Nisbet, Brossard and Nisbet2004). Scholars have long theorized about how cross-cutting pressures in an individual’s social environment may cause an individual to become less interested in politics and to disengage (Campbell et al. Reference Campbell, Converse, Miller and Stokes1960; Ithel de Sola Pool and Popkin Reference de Sola Pool, Abelson and Popkin1956). Recent work has tested theories about whether disagreement in an individual’s social network affects the political participation (Mutz Reference Mutz2002; Huckfeldt, Johnson, and Sprague Reference Huckfeldt, Johnson and Sprague2004). This work has consistently found that exposure to disagreement depresses engagement and participation.

Previous studies have relied on snowball samples in order to construct social network measures. Survey respondents may be biased in recalling their discussion partners. Social network sites, such as Facebook and Twitter, allow us to observe friendships without asking individuals about their political discussion partners. If bias in recalling discussion partners is associated with the difference in ideology between a pair of individuals, which certainly may be the case if such discussions are more likely to be memorable, estimates of its effect on participation may be biased as well.

We add to this literature by studying the relationship between exposure to disagreement and validated public voting records. We matched records for all individuals from 13 states (see Jones, Bond, Fariss, Settle, Kramer, Marlow, and Fowler (Reference Jones, Bond, Fariss, Settle, Kramer, Marlow and Fowler2012) for more information) to the Facebook data. Among the 6.2 million individuals for whom we calculated an ideology score, we match a voting record for 397,815,Footnote ⁸ resulting in a total of 2,410,097 friendship pairs where the individual and the friend had both an ideology score and a public record of whether each person voted. Here we test the relationship between an individual’s (in social network terms, the “ego”) turnout and the difference between the ego’s ideology and that of a friend (the “alter”). Our primary variable of interest is the Mean |Ideology Difference|, as it measures the average difference in ideology between the ego and all connected alters. As Table 4 shows, an increase in the ideological distance between friends is associated with lower rates of turnout by the ego. A one standard deviation increase in the Mean |Ideology Difference| measure corresponds to a 1.3 [95% CI 1.1, 1.4] percentage point decrease in the likelihood of voting (95% CI estimated based on King, Tomz, and Wittenberg Reference King, Tomz and Wittenberg2000).

TABLE 4. Logistic Regression of Ego Validated Voting in 2010 on Ego Covariates and Alter Characteristics of Ideology and Turnout.

Note: Robust standard errors clustered by common friend (e.g., Williams Reference Williams2000; Wooldridge Reference Wooldridge2002).

This result is consistent with previous work showing that disagreement in an individual’s social network is associated with lower turnout rates (Huckfeldt, Johnson, and Sprague Reference Huckfeldt, Johnson and Sprague2004; Mutz Reference Mutz2002). However, the present work has the advantage of gathering behavioral data. That is, we use validated public voting records, a behavioral ideology measure, and observe friendships, which help us avoid an array of issues including survey response bias and recall bias for friendships.