Not all undecided voters are alike: Evidence from an Israeli election

Yarchi, Moran; Wolfsfeld, Gadi; Samuel-Azran, Tal

doi:10.1016/j.giq.2021.101598

Cited by 11 publications

(5 citation statements)

References 54 publications

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“…That said, recent research suggests that low-familiarity (also called "low-information") voters differ in nontrivial ways from high-familiarity ("high-information") voters (Yarchi et al, 2021). Our 2014 Lok Sabha experiment suggests that low-familiarity voters may be more vulnerable to SEME than high-familiarity voters, and so does a set of experiments we recently conducted on what we call the "multiple exposure effect" (MEE) (Epstein et al, 2023).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

How a Daily Regimen of Operant Conditioning Might Explain the Power of the Search Engine Manipulation Effect (SEME)

Epstein,

Lothringer,

Zankich

2024

Behav. Soc. Iss.

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Recent studies have shown that biased search results can produce substantial shifts in the opinions and voting preferences of undecided voters – a phenomenon called the “search engine manipulation effect” (SEME), one of the most powerful list effects ever discovered. We believe this is so because, unlike other list effects, SEME is supported by a daily regimen of operant conditioning. When people conduct searches for simple facts (86% of searches), the correct answer invariably turns up in the top position, which teaches users to attend to and click on high-ranking search results. As a result, when people are undecided, they tend to formulate opinions based on web pages linked to top search results. We tested this hypothesis in a controlled experiment with 551 US voters. Participants in our High-Trust group conducted routine searches in which the correct answer always appeared in the first search result. In our Low-Trust group, the correct answer could appear in any search position other than the first two. In all, participants had to answer five questions during this pre-training, and we focused our analysis on people who answered all the questions correctly (n = 355) – in other words, on people who were maximally impacted by the pre-training contingencies. A difference consistent with our hypothesis emerged between the groups when they were subsequently asked to search for information on political candidates. Voting preferences in the High-Trust group shifted toward the favored candidate at a higher rate (34.6%) than voting preferences in the Low-Trust group (17.1%, p = 0.001).

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Section: Limitations and Future Researchmentioning

confidence: 99%

How a Daily Regimen of Operant Conditioning Might Explain the Power of the Search Engine Manipulation Effect (SEME)

Epstein,

Lothringer,

Zankich

2024

Behav. Soc. Iss.

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“…Disentangling the response class 'neither in favor nor against' from 'supportive ' and 'oppositional' responses is also highly interesting from a practical perspective. It can offer a unique perspective into the psychological drivers of a distinct group of potential voters: a group that might be swayed in either direction (support or opposition) and potentially be a deciding weight in actual voting outcomes (Yarchi, Wolfsfeld, & Samuel-Azran, 2021;Arcuri et al, 2008;Smidt, 2017). Moreover, sociological, economic, and cultural research suggests that contextual factors and processes can strongly influence how individuals and communities engage with the issue (Drews & van den Bergh, 2016;Lee et al, 2015;Dunlap, 1998).…”

Section: Mainmentioning

confidence: 99%

Predictors of climate change policy preferences: A machine learning approach

Uenal¹,

Kunst²,

Hudson³

et al. 2023

Preprint

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Climate change poses a significant threat to planetary and civilizational health. Fossil fuel taxation policies are an effective climate change mitigation strategy. Yet, public opinion research indicates that support for these climate change mitigation measures vary greatly across nations. Here, using machine learning techniques on a set of nationally representative surveys of 22 European countries and Israel, we determine the relative influence of a large number (151 predictors) of individual-level attitudes, beliefs, perceptions, and behaviors from six groups (i.e., [1] media and social trust, [2] politics, [3] subjective well-being, social exclusion, religion, national and ethnic identity, [4] attitudes towards climate change, [5] energy security and energy preferences, [6] welfare attitudes, [7] human values) and socio-demographic factors, as well as national-level indices of social, economic, ecological, and environmental development. Across nations, feeling personally responsible for reducing climate change, being concerned about climate change, and willingness to reduce one’s own energy usage are the three most frequent and most robust individual predictors of climate change mitigation policy support (vs. opposition). However, other key factors associated with policy support, such as general political- and welfare attitudes, highlight the need to develop tailored climate communication strategies for individual groups and nations. Moreover, results show substantial within-country variation when comparing undecided, supportive, and opposed respondents’ key predictors, indicating different sets of underlying psychological motivations. Fossil fuel consumption per capita and economic, environmental, ecological development indicators predict between-country dissimilarities.

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“…Last-minute broadcasts which inform viewers about elections are of particular interest for the discussion of factors affecting electoral choices ( Hofstetter & Buss, 1980 ). Such information is relevant for late deciding voters, the numbers of which have been rising in Western democracies, including in the US (see Yarchi et al (2021) for a list of countries). For example, on election day, 12.5% of 2016 US voters were either undecided or said they planned to vote for third-party candidates ( Silver, 2017 ).…”

Section: Media Coverage and Electionsmentioning

confidence: 99%

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

Ulloa

Makhortykh

Urman

et al. 2023

Social Science Computer Review

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The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. Using virtual agents that simulate human web browsing behavior to collect search engine result pages, we conduct a longitudinal study of news results of five search engines collected in short bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries (“joe biden,” “donald trump,” and “us elections”) compared to topical (e.g., “coronavirus”) or stable (e.g., “holocaust”) queries. We demonstrate that our method captures sudden changes in highly covered news topics as well as multiple differences across search engines and regions over time. We highlight novelty imbalances between candidate queries which affect their visibility during electoral periods, and conclude that, when it comes to news, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources that they rely on.

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Not all undecided voters are alike: Evidence from an Israeli election

Cited by 11 publications

References 54 publications

How a Daily Regimen of Operant Conditioning Might Explain the Power of the Search Engine Manipulation Effect (SEME)

How a Daily Regimen of Operant Conditioning Might Explain the Power of the Search Engine Manipulation Effect (SEME)

Predictors of climate change policy preferences: A machine learning approach

Novelty in News Search: A Longitudinal Study of the 2020 US Elections

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