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The Role of Personal Availability and Gender in Negative Online Congressional Campaigning


Negative campaigning in elections has received considerable attention. However, an important dimension of negative campaigning remains underexplored: the extent to which a candidate’s presentation of self affects their likelihood of receiving negativity. Work on gender differences in self-personalization and media personalization also suggests that this effect might be shaped by candidate gender. This paper investigates if a candidate using personal details in the service of campaign promotion increases the likelihood that the candidate will receive negativity from an opponent and if this association is moderated by candidate gender. Using congressional campaign website data from 2002 to 2006, evidence does not suggest that candidates who personalize online are any more likely to receive online negativity. Further, findings suggest that only female candidates see their likelihood of receiving online negativity vary as a function of online self-personalization. Female candidates have a higher likelihood of receiving online negativity from their campaign opponent when the candidate is more personable—that is, when they make information about their private selves more publicly available for negative framing at the hands of their opponent. Robustness checks reveal that this effect is not time independent, however, suggesting the personalization-gender-negativity relationship may be conditional on electoral context. Implications for work on personalization and negative campaigning, the role of gender in these processes, and campaign risk-taking are discussed.

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  1. It is worth noting, however, that evidence of the extent to which candidates are using their social and digital media platforms more for personalizing strategies than for traditional campaign content is mixed (McGregor et al., 2017, pp. 266–267).

  2. As Kahn (1996, p. 53) notes in the context of how voters perceive candidates: “If voters are provided with a great deal of information about a candidates’ personality characteristics, then voters are likely to consider this information when developing impressions of candidates.” Such is also the case with campaign opponents, with the added threat that opponents can then spin this information in their own constituent messaging strategies.

  3. Concerning selection biases in terms of candidates having personal campaign websites, Druckman and colleagues note that “[they] were able to identify almost all Senate candidate Web sites and nearly 95% of House sites in ...[the] sample. This suggests that while not all candidates had Web sites, clearly the overwhelming majority did” (2010b, p. 92).

  4. More information on the data collection procedure can be found in Druckman et al. (2010b, pp. 92–93).

  5. Indeed, though politically involved or engaged actors are more likely to visit campaign websites, “voters in general” and “undecided voters” remain the “primary target audiences” on these web platforms (Druckman et al., 2009, p. 346; Druckman et al., 2010b). For instance, there is a strong association between levels and likelihoods of negativity in both website- and television-based campaigning. As Druckman and colleagues note: “Forty-eight percent (351 of 732) of candidates went negative on the Web, compared to 55% in their television advertisements (128 of 232). Candidates are not more likely to go negative on the Web” (2010b, p. 95).

  6. It is interesting to note, however, that campaign strategists reported going negative less often on their websites for the 2016 election cycle than they did from 2008 to 2014 election cycles (Druckman et al., 2018b, p. 400).

  7. Results from the estimated regression models were similar when constructing this variable using principal components analysis and as a simple additive index. Cronbach’s alpha for the six-item additive scale was low (α = .36), which might reflect the fact that the underlying factor space between these six items is a higher dimensional one. The first two dimensions of the PCA space, for example, each have eigenvalues greater than one, and the second dimension in the MCA space accounts for a non-trivial 18% of the total inertia (\(\frac{\chi ^2}{n}\)) in the indicator matrix.

  8. Multiple correspondence analyses were carried out using the FactoMineR package (Husson et al., 2013) in the R statistical computing environment (Team et al., 2013). All visualizations in this paper were made using ggplot2 (Wickham, 2016).

  9. Though Druckman et al. (2010b) found that candidates were much more likely to go negative in their online campaigning in highly competitive races. However, in their study of negative campaigning in Senate races from 1988 to 1998, Lau and Pomper (2001) found that the competitiveness of a race was not a significant predictor of going negative after adjusting for other factors.

  10. Competitiveness was measured using The Cook Political Report (Druckman et al., 2010a, p. 347). Race competition ratings from these reports were used to classify each race according to whether it was “solid Democratic or Republican” (0), “likely Democratic or Republican” (.33), leaning Democratic or Republican” (.67), or a “toss-up” (1).

  11. The dataset also includes a measure of funds spend in U.S. dollars. However, there is a high bivariate correlation between the funds received and funds spent variables in the full sample (r = 0.987). As such, only the funds received variable is used in the analyses.

  12. The issues addressed on each candidate’s website were coded and organized into the following higher-order typology: defense, jobs and the economy, healthcare, education, group advocacy, environment, government reform, immigration, crime, moral/ethical issues, social security, taxes, and government spending (Druckman et al., 2010a, p. 9). These data were then compared to national public opinion polls to create “issue ownership” scores for each issue area. These scores were the estimated percent of the public who believed an issue was “owned” by Republicans or Democrats in that year. These issue scores were then summed per candidate depending on how many times they brought up a particular issue on their website. Each issue score in the summation was also signed according to the candidate’s party affiliation (where, e.g., a Democratic candidate gets a + for an issue they address that is owned more by Democrats and a - for an issue that is owned more by Republicans). The summation was then normalized by the total number of issues brought up on the candidate’s website. The resulting “Issue Ownership” variable reflects the extent to which a candidate in a given race addresses issues more associated with their party, where larger and more positive values indicate more party-aligned issue engagement and more negative values indicate less party-aligned issue engagement (Druckman et al., 2009, p. 349). To index issue salience, each issue area per year was scored as the percent of the public (in that year) that saw that issue area as one of the two most important issues facing the country using public opinion data on issue importance derived from Harris Interactive (Druckman et al., 2009, p. 361). These percentages were then summed for each candidate in a race depending on the number of times the candidate engaged the issue on the site and normalized by the number of issues discussed (Druckman et al., 2009, p. 361). The resulting “Issue Salience” variable is the mean public importance (in %) of the issues addressed by the candidate in that year.

  13. Logit models give similar results to the probit models.

  14. A random effects probit model with campaigns nested within candidates produced virtually identical results to the one-level probit model for the primary coefficients of interest. Lastly, to account for the possibility that the associations of interest here are primarily candidate-level associations rather campaign-level associations, I ran a linear between-effects model—where the proportion of a candidate’s total campaigns wherein they received negativity is regressed on candidate-level means for all covariates. The main coefficients of interest are the same in terms of direction, though the effect size is slightly attenuated when compared to the one-level probit model. However, an unconditional variance decomposition probit model (Singer et al., 2003, p. 75) suggested that very little of the total variance in the negativity reception variable is accounted for at the candidate level—about 10%. Taken together, a simple one-level probit model was chosen over random effects and between effects specifications. These multilevel results are available upon request.

  15. A version of the model with constituency fixed effects—states for Senate candidates and state districts for House candidates—provided similar coefficient estimates. The model fit, however, was poorer with constituency fixed effects (\(AIC_c\) = 796.761 and BIC = 1345.944, though AIC = 625.761) when compared with the reported model (see Table 2). This was also the case for the model 1.

  16. The \(AIC_c\) statistic—a version of AIC that adjusts for the tendency of AIC to suggest overfitting models in the presence of small sample sizes (Hurvich & Tsai, 1989)—was calculated as follows for each model: \(AIC_c = AIC + \frac{2k(k+1)}{n-k-1}\), where k is the number of estimated parameters (including the intercept) and n is the sample size (Burnham & Anderson, 2003, p. 66).

  17. The \(\Delta BIC_{a-m}\) does not suggest any sort of meaningful increase in model fit (\(\Delta BIC_{a-m}\) = − 2.131).

  18. Table versions of the models are available in Table 6 of Appendix 3.

  19. Special thanks to the anonymous reviewer who suggested this example.

  20. An incumbent was defined as a candidate who was neither a challenger nor an open-seat candidate.


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I would like to thank Rich Williams, Dustin S. Stoltz, Omar Lizardo, the anonymous reviewers, and editor for helpful comments on earlier drafts of this paper. I would also like to thank James N. Druckman for fielding my inquires about the dataset used here. Thanks also to Druckman and his colleagues—Michael Parkin and Martin Kifer—for making their data publicly available. A replication repository for this paper can be found here:

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Appendix 1: Regression Tables for Models #1 and #2

See Table 4.

Table 4 Probit Estimates of Negativity Reception, Models #1 and #2

Appendix 2: Challenger-Incumbent Sub-Sample Analyses

See Table 5.

Table 5 Probit Estimates of Negativity Reception, Study #2, Separate Challenger-Incumbent Sub-Samples

Appendix 3: Separate Year Analyses

See Table 6.

Table 6 Probit Estimates of Negativity Reception, by Election Year

Appendix 4: On the Choice of the Dependent Variable

The dependent variable used here conflates receiving personal negativity with receiving issue-based negativity. The dataset also includes dummy variables for whether or not the candidate themselves went negative at the level of the personal and/or at the level of issues. New variables can therefore easily be constructed that “match” each candidate with their opponent and report whether or not the candidate received personal negativity explicitly (net of issue-based negativity).

The issue, however, is this: these dummy variables only exist for the 2004 and 2006 cycles—the two election years where the relationship of interest (i.e., the relationship between negativity reception and the personalization-gender interaction) is not statistically significant (see the “Robustness Check of Temporal Stability” section of the paper). As such, for the election cycle where the association likely can’t be attributed solely to random chance, there is no way to disentangle the outcome variable into personal vs. issue-based sources of opponent negativity.

That said, I still created a new version of the outcome variable for the 2004 and 2006 elections—where 0 = did not receive personal negativity from the opponent and 1 = did receive personal negativity from the opponent. While the relationship is not statistically significant, the coefficients are signed in the expected directions for 2006 (\({\hat{\beta }}_{Availability}\) = 0.070; \({\hat{\beta }}_{Female-Male}\) = 0.013; \({\hat{\beta }}_{Availability \times Female}\) = 0.205).

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Taylor, M.A. The Role of Personal Availability and Gender in Negative Online Congressional Campaigning. Polit Behav (2021).

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  • Negative campaigning
  • Online campaigning
  • Content analysis
  • Gender
  • Impression formation
  • Personalization