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Effects of issue and poll news on electoral volatility: conversion or crystallization?

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Abstract

In the last decades, electoral volatility has been on the rise in Western democracies. Scholars have proposed several explanations for this phenomenon of floating voters. Exposure to media coverage as a short-term explanation for electoral volatility has of yet been understudied. This study examines the effect of media content (issue news and poll news) on two different types of vote change: conversion, switching from one party to another, and crystallization, switching from being undecided to casting a vote for a party. We use a national panel survey (N = 765) and link this to a content analysis of campaign news on television and in newspapers during national Dutch elections. Findings reveal that exposure to issue news increases the chance of crystallization, whereas it decreases the chance of conversion. Conversely, exposure to poll news increases the chance of conversion, whereas it decreases the chance of crystallization.

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Fig. 1

Note: The graphs show the predicted probability for respondents with average level of news exposure and the predicted probability for respondents with one and two standard deviations above and below the mean. Estimates are calculated while keeping the other variables at observed values

Fig. 2

Note: The graphs show the predicted probability for respondents with average level of news exposure and the predicted probability for respondents with one and two standard deviations above and below the mean. Estimates are calculated while keeping the other variables at observed values

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Notes

  1. Panel attrition does not seem to affect our findings. Most respondents dropped out between May and June. Those are probably the respondents who found it too much effort to participate in the whole panel survey. In the other waves, the recontact rate is very high. The respondents that we finally included in our study did not differ a lot from the drop-outs on the most important variables, such as political interest and media use.

  2. One could argue that media effects on volatility already occur earlier in the campaign. Therefore, we tested whether voters also converted or crystallized between t-2 and t-1 after 1 week of exposure to campaign news. Yet, no significant results were found. This implies that voters change their vote intention only later in the campaign when they have been exposed to a certain degree of campaign news.

  3. A representative sample (1537 persons) was selected. The respondent data of the 765 persons who completed the survey in all waves mirror census data by and large in terms of age, gender (49.5% male in census data, compared to 49.7% male in the sample), and education (maximum deviation of the sample from census data of 2% per education category). Older respondents (65–80) are slightly overrepresented in our sample (15.8% in census data, compared to 21.1% in the sample).

  4. Volatility on the individual level can be operationalized in several ways. A common method is to construct dummy variables based on whether a voter changes party choice (“1”) or not (“0”). Yet, studies differ in which responses they regard as a change (Dassonneville 2011; Dilliplane 2014; Van der Meer et al. 2015).

  5. For both the conversion and the crystallization outcome, respondents were only assigned a “1” if they actually voted for a party at t. A switch from or to “other, namely…” from or to another party is treated as a conversion switch. For the crystallization variable, we treat a switch from “don’t know,” “blank,” “abstain,” and “refuse” to a party choice as a crystallization switch. Only “refuse” in the last wave and “no right to vote” were treated as missing.

  6. We also wanted to include political cynicism as a control variable, as several studies found that this is an important predictor of volatile voting behavior (e.g., Dassonneville 2011; Adriaansen et al. 2012). Yet, due to missing values on this variable, we decided to not include political cynicism in the analyses. When we do include political cynicism in the analyses, we find no effects of political cynicism on either crystallization or conversion.

  7. Recent research has found that people in the middle of the political spectrum are most volatile (Van der Meer et al. 2015).

  8. Although we only look at vote switching from t-1 to t, it is likely that respondents were already influenced by campaign news that appeared before t-1. Therefore, we include campaign news as from August 22. Since the election campaign started later due to summer recess, we only use content analysis of the last 3 weeks of the campaign.

  9. The unit of analysis are separate news articles in newspapers or news items in television programs. This approach does not take into account the length of an article or item, nor does it provide the opportunity to identify specific issues or actors at the sentence level. One could argue that this could lead to an under- or overestimation of the presence of content characteristics. However, since we are interested in the overall presence of issue and poll news, and not so much in the presence of specific issues or actors, selecting full articles or new items as the unit of analysis is a suitable approach for this study.

  10. For newspaper reading, we also included exposure to newspaper websites.

  11. Although scholars are still debating on the most reliable and valid measure of media exposure, they agree that this measure of exposure per medium overcomes at least some of the limitations of conventional news exposure measures (for a more elaborate discussion see, Dilliplane et al. 2013; Slater 2007).

  12. By employing the average exposure to media content instead of the sum, we control for potential overreporting of news exposure (see criticisms on self-reported news exposure measures, Prior 2009).

  13. We computed issue news exposure and poll news exposure separately for newspapers and television for two reasons. First, the content analysis for newspapers differs from the content analysis for television programs in its design. Whereas the unit of analysis for newspapers is clearly distinguished by separate news articles, the unit of analysis for television programs is decided upon for each television program based on content and form. Some television programs, like the news, clearly switch between topics. In other television programs, the distinction between topics is less clear, and items can be identified by devised interruptions like, for instance, a commercial break. Secondly, to test whether television programs differ from newspapers in the amount of attention they pay to issue news and poll news, an independent samples t test was performed. The results show that the average attention to issue news was significantly higher [t(13) = − 2.22, p = 0.045] in newspapers (M = 0.39, SD = 0.05) than on television (M = 0.25, SD = 0.15). The average attention to poll news is higher on television (M = 0.24, SD = 0.14) than in newspapers (M = 0.14, SD = 0.03), yet this difference is not statistically significant [t(13) = 1.68, p = 0.116]. However, since we find a significant difference for issue news, and taking into account that the unit of analysis was different for both media, we decided to compute issue news exposure and poll news exposure for newspapers and television separately.

  14. As robustness check, we also estimated a multinomial regression model in which the dependent variables are constructed slightly different, with ‘stable’ and ‘abstention’ being collapsed into one category. In this model, the effects of issue and poll news on crystallization were largely similar, yet marginally significant. The effects of issue and poll news on conversion hold. Furthermore, the effect of issue news in newspapers on conversion was positive and significant in this model. As a second robustness check we also estimated the effects on crystallization and conversion in binary logistic regression models. The results were largely similar to the ones reported in Table 2, except the effect of poll news in newspapers on crystallization was marginally significant in the binary logistic regression model.

  15. We also estimated multinomial regressions models including general newspaper and television exposure variables (instead of content exposure variables). Neither newspaper exposure, nor television exposure had an effect on either crystallization or conversion. We can thus assume that the media effects we find can be ascribed to the differences in content and not to the differences in media.

  16. We also estimated multinomial regression models in which issue news exposure and poll news exposure are not separated for newspapers and television. In these models we still find a significant positive effect for issue news on crystallization. However, we find no effects of poll news and on conversion, which is not that surprising as our results show that the effects of both media are contradictory.

  17. Since we know that there can be individual-level variation in the way media influences voters (Valkenburg and Peter 2013; Zaller 1991), we ran an additional analysis to test the interaction effect between political interest and the media exposure variables. The findings revealed a marginally significant effect of issue news and poll news on conversion for moderately interested voters. Voters with moderate levels of political interest remain when exposed to issue news, but convert to another party when exposed to poll news.

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Authors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Sabine Geers, Linda Bos & Claes H. de Vreese

  2. Amsterdam School of Communication Research ASCoR, University of Amsterdam, Postbus 15791, 1001 NG, Amsterdam, The Netherlands

    Sabine Geers

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  1. Sabine Geers
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Correspondence to Sabine Geers.

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Geers, S., Bos, L. & de Vreese, C.H. Effects of issue and poll news on electoral volatility: conversion or crystallization?. Acta Polit 54, 521–539 (2019). https://doi.org/10.1057/s41269-018-0089-x

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