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Online discussions about politics and current events play a growing role in public life, and they can foster positive outcomes (e.g., civic engagement and political participation) and negative outcomes (e.g., hostility and polarization). The present research examines how the use of doubtful (vs. confident) language influences behavior in online discussions of current events. We examine the effects of doubtful language on comment popularity (i.e., recommendations from other users) and the use of emotional language in subsequent replies. We examine data from 1.9 million user comments from the New York Times website. Comments containing doubtful language were less popular, receiving fewer user recommendations. Additionally, replies to doubtful comments were less emotional (containing fewer positive emotions and fewer negative emotions). These results suggest that although doubtful authors are less likely to be recommended by other users, they may play an important role in helping to foster civility in online discussions.

The current work estimated the relative importance of joke and audience characteristics for the occurrence of amusement. Much psychological research has focused on stimulus characteristics when searching for sources of funniness. Some researchers have instead highlighted the importance of perceiver characteristics, such as dispositional cheerfulness. Across five preregistered studies (Ns = 118–54,905) with varied stimuli and perceiver samples (website visitors, students, Mechanical Turk and Prolific users), variance-decomposition analyses found that perceiver characteristics account for more variance in funniness ratings than stimulus characteristics. Thus, psychological theories focusing on between-persons differences have a relatively high potential for explaining and predicting humor appreciation (here, funniness ratings). Crucially, perceiver-by-stimulus interactions explained the largest amount of variance, highlighting the importance of fit between joke and audience characteristics when predicting amusement. Implications for humor-appreciation theories and applications are discussed.

Machine learning methods for prediction and pattern detection are increasingly prevalent in psychological research. We provide an introductory overview of machine learning, its applications, and describe how to implement models for research. We review fundamental concepts of machine learning, such as prediction accuracy and out-of-sample evaluation, and summarize standard prediction algorithms including linear regressions, ridge regressions, decision trees, and random forests (plus additional algorithms in the supplementary materials). We demonstrate each method with examples and annotated R code, and discuss best practices for determining sample sizes; comparing model performances; tuning prediction models; preregistering prediction models; and reporting results. Finally, we discuss the value of machine learning methods in maintaining psychology’s status as a predictive science.

27 april 2020, Contains fulltext : 220488.pdf (Publisher’s version ) (Open Access), Background. The amount of random chance involved in multiplayer competitions varies depending on the structure of the competition. A hugely popular mode of competition is the Battle Royale, in which a group of participants semi-randomly encounters and battles each other until only one player is left. We utilized computer simulations to investigate how strongly the outcomes of different Battle Royale formats reflect the actual distribution of skill among the players. Further, we investigated which game features affect the congruency between skills and results. Method. We build agent-based models in NETLOGO to simulate the outcomes of different Battle Royale formats. Results. Our results consistently forwarded that outcomes from Battle Royale are only weakly aligned with actual player skills. Conclusion. Our findings suggest that many forms of Battle Royale involve substantially more luck than other sports or e-sports competitions. Further, game developers should note that introducing new game features can entail both higher and lower involvement of skill over luck, and that these features can be tuned to obtain a desired level of uncertainty.

How do expressions of doubt affect trust in online reviews? Some previous studies find that people trust confident advisors more than doubtful advisors, whereas others find doubtful advisors are trusted more. We tested the effects of expressing doubt using Yelp data and in a controlled experiment: In Study 1, reviews from Yelp (N = 5.9 million) were coded using the Linguistic Inquiry Word Count (LIWC) software. Reviews with doubtful language were seen as more useful, and this result was robust when controlling for other psychological and linguistic variables. In Study 2, participants (N = 660) evaluated reviews with doubtful or confident conclusions; doubtful reviews were seen as more likely to be written by actual consumers. In both studies, the positive effects of doubt were stronger for positive (vs. negative) reviews, suggesting doubt mitigates concerns about fake positive reviews. The present study emphasizes the advantages of expressing doubt.

We review online activism and its relations with offline collective action. Social media facilitate online activism, particularly by documenting and collating individual experiences, community building, norm formation, and development of shared realities. In theory, online activism could hinder offline protests, but empirical evidence for slacktivism is mixed. In some contexts, online and offline action could be unrelated because people act differently online versus offline, or because people restrict their actions to one domain. However, most empirical evidence suggests that online and offline activism are positively related and intertwined (no digital dualism), because social media posts can mobilise others for offline protest. Notwithstanding this positive relationship, the internet also enhances the visibility of activism and therefore facilitates repression in repressive contexts.

To what extent are intergroup attitudes associated with regional differences in online aggression and hostility? We test whether regional attitude biases towards minorities and their local variability (i.e. intraregional polarization) independently predict verbal hostility on social media. We measure online hostility using large US American samples from Twitter and measure regional attitudes using nationwide survey data from Project Implicit. Average regional biases against Black people, White people, and gay people are associated with regional differences in social media hostility, and this effect is confounded with regional racial and ideological opposition. In addition, intraregional variability in interracial attitudes is also positively associated with online hostility. In other words, there is greater online hostility in regions where residents disagree in their interracial attitudes. This effect is present both for the full resident sample and when restricting the sample to White attitude holders. We find that this relationship is also, in part, confounded with regional proportions of ideological and racial groups (attitudes are more heterogeneous in regions with greater ideological and racial diversity). We discuss potential mechanisms underlying these relationships, as well as the dangers of escalating conflict and hostility when individuals with diverging intergroup attitudes interact.

Sometimes interesting statistical findings are produced by a small number of “lucky” data points within the tested sample. To address this issue, researchers and reviewers are encouraged to investigate outliers and influential data points. Here, we present StatBreak, an easy-to-apply method based on a genetic algorithm, which identifies the observations that most strongly contributed to a hypothesized finding (e.g., effect size, model fit, p-value, Bayes factor). Within a given sample, StatBreak searches for the largest subsample in which a previously observed pattern is not present or reduced below a specifiable threshold. Thus, it answers the question: “Which (and how few) ‘lucky’ cases would need to be excluded from a given sample for data-based conclusion to change?” StatBreak consists of a simple R-function and flags the luckiest data points for any form of statistical analysis. Here, we demonstrate the effectiveness of the method with simulated and real data across a range of study designs and analyses. Additionally, we describe StatBreak’s R-function and explain how researchers and reviewers can apply the method to the data they are working with.