Wednesday, January 27, 2021

Changes in sexual behavior during of the COVID-19 pandemic and physical distancing measures in single and partnered participants in Germany, Switzerland and Austria

The disruptive impact of the COVID-19 pandemic on sexual behavior of a German-speaking population. Z. Hille, U.C. Oezdemir, K.M. Beier, L. Hatzler. Sexologies, January 27 2021.


Objective: The aim of this study was to investigate changes in sexual behavior during of the COVID-19 pandemic and physical distancing measures in single and partnered participants in Germany, Switzerland and Austria.

Material and methods: Participants were assessed in a cross-sectional online survey. Amongst others, sociodemographic data, sociosexual attitudes as well as engagement in a range of sexual activities and practices prior to and during the pandemic were collected. Additionally, for subjects in a relationship, sexual attraction to the partner (feelings of affection during partnered sexual activities, and physical sexual attraction) and relationship satisfaction were measured.

Results: Data of 1017 single and 1498 partnered participants were analyzed. Partnered participants masturbated significantly less during physical distancing measures compared to the period before, whereas single males masturbated more often. Single females masturbate less frequently but this difference was not statistically significant. For both subgroups, the frequency of most sexual activities significantly declined since the beginning of physical distancing measures with anal intercourse in partnered participants being the only exception that showed no significant decrease. In the group of participants in relationships, sociosexual variables and physical sexual attraction to one's partner showed a significant positive relationship to the number of new sexual practices added during physical distancing measures, while feelings of affection during partnered sexual activities and relationship satisfaction did not.

Conclusion: Our data support previous findings showing potential disruptive effects on sexual routines of single and partnered participants by the COVID-19 pandemic and physical distancing measures. Further studies are needed to reveal causal factors and to study long-term effects on mental health and relationships.

The Impact of the Economy on Presidential Elections Throughout US History: Voters, we find, appear to judge incumbent presidents on the economy all the way back to George Washington

The Impact of the Economy on Presidential Elections Throughout US History. Eric Guntermann, Gabriel S. Lenz & Jeffrey R. Myers. Political Behavior, Jan 27 2021.

Rolf Degen's take: "It's always been the economy, stupid, all the way back to George Washington."

Abstract: As numerous studies in the US and elsewhere document, voters often hold incumbents accountable for recent economic circumstances. However, our knowledge of the conditions that allow voters to do so remains incomplete. In particular, most findings about economic voting come from studies of modern economies (post World War II). Modern economies have a host of characteristics that seem to lend themselves to economic voting. Their governments play a large role in the economy and have the Keynesian toolset necessary to influence the economy. Their voters are educated and have access to detailed economic data from ubiquitous media. Are these and other modern conditions necessary for economic voting? Would voters still hold politicians accountable even under adverse conditions? Using economic measures now available back to the 1790s, we study economic voting from the earliest days of the US Republic when none of these conditions were met. Voters, we find, appear to judge incumbent presidents on the economy all the way back to George Washington. Consistent with this pattern, we also find that the economy appears to shape presidents' decisions to run again throughout US history. These findings support recent comparative evidence that economic voting is pervasive across a variety of contexts.

Sperm donation: Mostly altruistic, donors favored anonymity; education level, conscientiousness, empathic concern were positively correlated to donation; age, & conservative and religious values were negatively associated

Motivations and Attitudes of Men Towards Sperm Donation: Whom to Donate and Why? João Areias, Jorge Gato & Mariana Moura-Ramos. Sexuality Research and Social Policy, Jan 27 2021.


Background: The widespread access to medically assisted reproduction (MAR) techniques for all women, regardless of any infertility diagnosis, has led to an increased, but as yet unmet, demand for sperm donors in Portugal. For this study, we deployed an online survey to explore men’s motivations for donating and their attitudes toward anonymity and donating for specific groups.

Method: The study’s sample comprised men who were eligible to donate sperm (N = 282). The relationships between these factors and participants’ psychological and sociodemographic characteristics were also explored.

Results: The results mostly indicated altruistic reasons for donating, positive attitudes toward anonymity, and a greater willingness to donate to infertile women. Overall, sexual orientation was not associated with the participants’ attitudes and motivations. Age, education level, conscientiousness, empathic concern, and conservative and religious values were associated with the participants’ motivations and attitudes toward sperm donation.

Conclusion: Recruitment campaigns should therefore consider the specific motivations, attitudes, and psychosocial characteristics of potential sperm donors. Indeed, parenthood is a universal right, so sperm donation should be encouraged, regardless of recipients’ fertility status. Clear information about the identifiability of sperm donors should also be provided.


The main goal of this study was to highlight what drives men to donate sperm, their attitudes toward anonymity, and their attitudes toward donating for specific groups, as well as to investigate how sexual orientation, psychological traits, and sociodemographic characteristics influence these motivations and attitudes. For reasons of clarity, the results will be discussed in terms of (i) motivations to donate, (ii) attitudes toward anonymity, and (iii) attitudes toward donating for specific groups.

Motivations to Donate

Consistent with the literature, altruistic motivation seems to be the most significant motivation for donating sperm, emphasizing a desire to help childless couples have children (Ekerhovd et al., 2008; Hedrih & Hedrih, 2012; Thijssen et al., 2017; Van der Broeck et al., 2013) and diminishing the role of financial compensation in return for donation (Ekerhovd et al., 2008; Hedrih & Hedrih, 2012; Thijssen et al., 2017). Motivations to donate were generally not associated with the participants’ sexual orientation, except for the motivation of “knowing one’s fertility,” with heterosexual men assigning more importance to this than other men. Non-heterosexual men’s lack of interest in their fertility status may be because they generally show a weaker intent to parent children and anticipate stigma upon achieving parenthood (Gato, Leal, Coimbra, & Tasker, 2020). Furthermore, many non-heterosexual men seem to be unfamiliar with alternative paths to parenthood (Patterson & Riskind, 2010), and some still envisage parenting as a feminine role (Gato & Fontaine, 2017; Pelka, 2009). Nevertheless, future research should further examine the genesis of these differences.

Age was negatively associated with the motivation of “knowing one’s fertility,” with younger men assigning more importance to this motivation. According to Thijssen et al. (2017), this association is expected because younger, childless men have not yet confirmed their fertility status. Furthermore, the men with a lower level of education also valued this motivation more, so future research should explore this association.

Considering the psychological characteristics of the participants, men with higher levels of Conscientiousness assigned more importance to learning their fertility status. People with high levels of conscientiousness may be more motivated to donate based on willingness to pass on their “good genes” (Thijssen et al., 2017), because this will confirm their purpose and usefulness to society and themselves. Similarly, men with higher levels of Conservative values also ascribed more importance to learning their fertility status. This is unsurprising if we consider that more conservative individuals tend to value social order and assign more importance to traditional institutions (Schwartz, 19921994), such as heteronormative families where children generally have a genetic link with their parents.

People with high levels of empathic concern donate in various contexts, and they are compassionate toward others and seem to be oriented toward alleviating the suffering of others in need (Verhaert & Van den Poel, 2011). This explains the positive association between empathic concern and the motivation to help others have a child, as was found in this study. Conversely, religious values were negatively correlated with this motivation. Even though modern Portugal is a secular country, Catholic values still exert a certain influence (Dix, 2010). In our study, the negative association between adherence to religious values and helping someone to have a child may derive from the fact that Catholicism tends to reject alternative family configurations, including the pursuit of pregnancy through MAR (Rubio, 2015).

Attitudes Toward Anonymity

Willingness to be contacted by the child was the least-indicated attitude, differing from the assertions “parents should disclose DI conception with the child” and “the institution where DI was realized can provide information about me to the child, since that information does not identify me.” This finding agrees with research that found that men tend to avoid donation if their identities may be disclosed (Bay et al., 2014; Thijssen et al., 2017). Nevertheless, a relatively positive view about non-anonymous donation was noticeable, because all participants endorsed disclosing DI conception to the child and releasing non-identifying information about the donor. No differences in attitudes toward anonymity were found as a function of sexual orientation, thus contradicting the study of Freeman et al. (2016), which verified that gay and bisexual men, when compared to their heterosexual peers, were more open minded when it came to anonymity and more willing to have contact with children conceived using their sperm. This study’s results refuted our hypotheses about differences between heterosexual and non-heterosexual men for attitudes toward sperm donation.

For sociodemographic characteristics, age was negatively associated with the attitude that “parents should disclose DI conception to the child,” with younger participants subscribing more to this attitude. According to Riggs and Russell (2011), men under the age of 26 preferred identity-release legislation, and this may reflect in the opinion that parents should disclose DI conception to the child. No psychological characteristics were associated with attitudes toward anonymity, perhaps due to the choice of psychological correlates in this study. Future research should therefore include other aspects, such as neuroticism and agreeableness.

Attitudes Toward Donating for Specific Groups

Regarding attitudes toward donating for specific groups, a positive tendency toward donating to all groups was discernible, and this is consistent with the findings of other studies (Bay et al., 2014; Ekerhovd et al., 2008; Thijssen et al., 2017). Nevertheless, donating to heterosexual women with fertility problems was the group regarded the most positively. This may come from the altruistic motivations to donate that participants reported. Indeed, altruism is based on a desire to help someone to have a child (Ekerhovd et al., 2008; Hedrih & Hedrih, 2012; Thijssen et al., 2017; Van der Broeck et al., 2013), and infertile women may be perceived, a priori, as being at a greater disadvantage.

Empathic concern was positively associated with donating to most recipient groups. This likely derives from the fact that people with high levels of empathic concern tend to focus more on alleviating the suffering of others and showing compassion (Verhaert & Van den Poel, 2011). In contrast, adherence to religious values was negatively associated with donating to all groups. As mentioned earlier, this may stem from the influence of Catholic views about family (Rubio, 2015).

Limitations, Future Directions, and Implications for Practice

Like any research endeavor, this study is not without limitations. More than half of our sample was highly educated, so it was not representative of the wider Portuguese population. Additionally, the imbalance in the number of heterosexual and non-heterosexual men may have further compromised the results and prevented us from drawing conclusions about the influence of this variable. The small magnitude of effects (except in the RM ANOVAs) also raised concerns and somewhat limited generalization of the findings.

Despite these limitations, however, our findings do have implications for practice. By shedding light on the motivations, attitudes, and characteristics of possible candidate donors, the findings of this study may inform future recruitment campaigns. Indeed, based on these findings, it could be argued that campaigns should target specific motivations, attitudes, and characteristics of potential donors. As Ferguson, Atsma, de Kort, and Veldhuizen (2012) reported in the context of blood donation, emphasizing the positive feelings that are associated with donation can increase donation rates, so a similar concept could be applied in the context of sperm donation. For example, in this study, the most frequently reported motivation to donate was to help someone have a child, which is an altruistic motivation. It may therefore be useful to highlight the altruistic nature of donation in any campaign.

This study also contributes to the view that withdrawing donation anonymity, which took place in Portugal in 2018, may not necessarily affect the number of potential donors, thus reflecting the positive trend toward non-anonymity reported by other studies (Bay et al., 2014; Thijssen et al., 2017). However, given a pattern of disengagement when the disclosed information is too personal or when there is a chance of contact with the child, the issue of anonymity should be further debated and clarified. Future recruitment campaigns should therefore stress that upon release of anonymity, the donor bears no obligation or commitment to the child. Likewise, it is crucial to emphasize that parenthood is a universal right in order to encourage sperm donation for any person or couple, regardless of fertility status or sexual orientation.

We showed that 46% of the variability in sleep duration & 44% of the variability in sleep quality is genetically determined; the remaining variation in the sleep characteristics can mostly be attributed to the unique environment

Heritability of Sleep Duration and Quality: A Systematic Review and Meta-analysis. Desana Kocevska et al. Sleep Medicine Reviews, January 27 2021, 101448.

Summary: Epidemiological and interventional research has highlighted sleep as a potentially modifiable risk factor associated with poor physical and mental health. Emerging evidence from (behavioral) genetic research also shows that sleep characteristics are under strong genetic control. With this study we aimed to meta-analyze the literature in this area to quantify the heritability of sleep duration and sleep quality in the general population. We conducted a systematic literature search in 5 online databases on January 24th 2020. Two authors independently screened 5,644 abstracts, and 160 complete articles for the inclusion criteria of twin studies from the general population reporting heritability statistics on sleep duration and/or quality, and written in English. We ultimately included 23 papers (19 independent samples: 45,328 twins between 6 months to 88 years) for sleep duration, and 13 papers (10 independent samples: 39,020 twins between 16 and 95 years) for sleep quality. Collectively, we showed that 46% of the variability in sleep duration and 44% of the variability in sleep quality is genetically determined. The remaining variation in the sleep characteristics can mostly be attributed to the unique environment the twins experience, although the shared environment seemed to play a role for the variability of childhood sleep duration. Meta-analyzed heritability estimates for sleep duration, however, varied substantially with age (17% infancy, 20-52% childhood, 69% adolescence and 42-45% adulthood) and reporter (8% parent-report, 38-52% self-report). Heritability estimates for actigraphic and PSG-estimated sleep were based on few small samples, warranting more research. Our findings highlight the importance of considering genetic influences when aiming to understand the underlying mechanisms contributing to the trajectories of sleep patterns across the lifespan.

Keywords: Behavioral GeneticsSleepGenesHeritabilityInheritance PatternsSleep durationSleep Quality

Check also What Do People Know About the Heritability of Sleep? Juan J. Madrid-Valero, Robert Chapman, Evangelina Bailo, Juan R. Ordoñana, Fatos Selita, Yulia Kovas & Alice M. Gregory. Behavior Genetics, Jan 23 2021.

Over just 10 years, both implicit and explicit male-science/female-arts and male-career/female-family stereotypes have shifted toward neutrality, weakening by 13%–19%, in all US regions & several other countries

Patterns of Implicit and Explicit Stereotypes III: Long-Term Change in Gender Stereotypes. Tessa E. S. Charlesworth, Mahzarin R. Banaji. Social Psychological and Personality Science, January 27, 2021.

Rolf Degen's take: "Over just 10 years, both implicit and explicit gender stereotypes have have moved toward neutrality worldwide."

Abstract: Gender stereotypes are widely shared “collective representations” that link gender groups (e.g., male/female) with roles or attributes (e.g., career/family, science/arts). Such collective stereotypes, especially implicit stereotypes, are assumed to be so deeply embedded in society that they are resistant to change. Yet over the past several decades, shifts in real-world gender roles suggest the possibility that gender stereotypes may also have changed alongside such shifts. The current project tests the patterns of recent gender stereotype change using a decade (2007–2018) of continuously collected data from 1.4 million implicit and explicit tests of gender stereotypes (male-science/female-arts, male-career/female-family). Time series analyses revealed that, over just 10 years, both implicit and explicit male-science/female-arts and male-career/female-family stereotypes have shifted toward neutrality, weakening by 13%–19%. Furthermore, these trends were observed across nearly all demographic groups and in all geographic regions of the United States and several other countries, indicating worldwide shifts in collective implicit and explicit gender stereotypes.

Keywords: implicit social cognition, gender stereotypes, stereotype change, time series analyses (ARIMA)

Following Twitter: Anger, but not the other constructs, was distinctly reflected in followed accounts, and there was some indication of bias in predictions for women but not for racial/ethnic minorities

Predicting Mental Health From Followed Accounts on Twitter. Cory Costello; Sanjay Srivastava; Reza Rejaie; Maureen Zalewski. Psychology (2021) 7 (1): 18731.

Rolf Degen's take: "The accounts you follow on Twitter could shed some light on your mental health."

Abstract: The past decade has seen rapid growth in research linking stable psychological characteristics (i.e., traits) to digital records of online behavior in Online Social Networks (OSNs) like Facebook and Twitter, which has implications for basic and applied behavioral sciences. Findings indicate that a broad range of psychological characteristics can be predicted from various behavioral residue online, including language used in posts on Facebook (Park et al., 2015) and Twitter (Reece et al., 2017), and which pages a person ‘likes’ on Facebook (e.g., Kosinski, Stillwell, & Graepel, 2013). The present study examined the extent to which the accounts a user follows on Twitter can be used to predict individual differences in self-reported anxiety, depression, post-traumatic stress, and anger. Followed accounts on Twitter offer distinct theoretical and practical advantages for researchers; they are potentially less subject to overt impression management and may better capture passive users. Using an approach designed to minimize overfitting and provide unbiased estimates of predictive accuracy, our results indicate that each of the four constructs can be predicted with modest accuracy (out-of-sample R’s of approximately .2). Exploratory analyses revealed that anger, but not the other constructs, was distinctly reflected in followed accounts, and there was some indication of bias in predictions for women (vs. men) but not for racial/ethnic minorities (vs. majorities). We discuss our results in light of theories linking psychological traits to behavior online, applications seeking to infer psychological characteristics from records of online behavior, and ethical issues such as algorithmic bias and users’ privacy.

Keywords:emotions, social network analysis, online social networks, machine learning, data science, mental health


Our central aim was to understand how mental health is reflected in network connections in social media. We did so by estimating how well individual differences in mental health can be predicted from the accounts that people follow on Twitter. The results showed that it is possible to do so with moderate accuracy. We selected models in training data using 10-fold cross-validation, and then we estimated the models’ performance in new data that was kept completely separate from training, where model Rs of approximately .2 were observed. Although these models were somewhat accurate, when we examined which features were weighted as important for prediction, we did not find them to be readily interpretable with respect to prior theories or broad themes of the mental health constructs we predicted.

Mental Health and the Curation of Social Media Experiences

This study demonstrated that mental health is reflected in the accounts people follow to at least a small extent. The design and data alone cannot support strong causal inferences. One interpretation that we find plausible is that the results reflect selection processes. The list of accounts that a Twitter user follows is a product of decisions made by the user. Those decisions are the primary way that a user creates their personalized experience on the platform: when a user browses Twitter, a majority of what they see is content from the accounts they previously decided to follow. It is thus possible that different mental health symptoms affect the kind of experience people want to have on Twitter, thus impacting their followed-account list. The straightforward ways this could play-out that we discussed at the outset of this paper – e.g., face-valid information-seeking via mental health support or advocacy groups, homophily (following others who display similar mental health symptoms), or emotion regulation strategies – did not seem to be supported. Instead, the accounts with high importance scores were celebrities, sports figures, media outlets, and other people and entities from popular culture. In some rare instances, these hinted towards homophily or a similar mechanism: for example, one account with a high importance score for depression was emo-rapper Lil Peep, who was open about his struggles with depression before his untimely death. More often, however, the connections were even less obvious, and few patterns emerged across the variety of highly important predictors. Other approaches, such as qualitative interviews or experiments that manipulate different account features, may be more promising in the future for shedding light on this question.

Causality in the other direction is also plausible: perhaps following certain accounts affects users’ mental health. For example, accounts that frequently tweet depressing or angry content might elicit depression or anger in their followers in a way that endures past a single browsing session. The two causal directions are not mutually exclusive and could reflect person-situation transactional processes, whereby individual differences in mental health lead to online experiences that then reinforce the pre-existing individual differences, mirroring longitudinal findings of such reciprocal person-environment transactions in personality development (Le et al., 2014; Roberts et al., 2003). Future longitudinal studies could help elucidate whether similar processes occur with mental health and social media use.

In a set of exploratory analyses, we probed the extent to which the predicted scores were capturing specific versus general features of psychopathology. The followed-account scores that were constructed to predict anger captured variance that was unique to that construct; but for depression, anxiety, and post-traumatic stress, we did not see evidence of specificity. One possible explanation is that followed accounts primarily capture a more general psychopathology factor (Lahey et al., 2012; Tackett et al., 2013) but anger also has distinct features that are also relevant. Another possibility is that followed accounts can distinguish between internalizing and externalizing symptoms, and anger appeared to show specificity since it was the only externalizing symptom we examined. The present work cannot distinguish between these possibilities, but future work including more externalizing symptoms may be helpful in differentiating between these and other possibilities.

Relevance for Applications

What does this degree of accuracy – a correlation between predicted and observed scores of approximately .2 – mean for potential applications? First, it’s worth noting that our conclusions are limited to twitter users that meet our minimal activity thresholds (25 tweets, 25 followers, 25 followed accounts), so they may not be applicable to twitter users as a whole, including truly passive users that might follow accounts but not tweet (at all). Even among the users that do meet these thresholds, we do not believe these models are accurate enough for use in individual-level diagnostic applications, as they would provide a highly uncertain, error-prone estimate of any single individual’s mental health status. At best, a correlation of that size might be useful in applications that rely on aggregates of large amounts of data. For example, this approach could be applied to population mental health research to characterize trends in accounts from the same region or with other features in common.

A caveat is that the goal of the present study was to focus on followed accounts – not to maximize predictive power by using all available information. It may be possible to achieve greater predictive accuracy by integrating analyses of followed accounts with complementary approaches that use tweet language and other data. In addition, more advanced approaches that would be tractable in larger datasets, such as training vector embeddings for followed accounts (analogous to word2vec embeddings; Mikolov et al., 2006), could help increase accuracy and should be investigated in the future. Likewise, it may be possible to leverage findings from recent work identifying clusters or communities of high in-degree accounts (Motamedi et al., 2020, 2018) to identify important accounts or calculate aggregate community scores, as opposed to the bottom-up approaches to filtering and aggregating accounts used in this study. Future work can examine the extent to which these different modifications to our procedure maximize predictive accuracy.

Another important caveat to consider with respect to possible applications of this work is that this approach is more suited to studying more stable individual differences in mental health rather than dynamic, within-person fluctuations or responses to specific events. This was an aim that was reflected in the design of this study – for example, the wording of the mental health measures covered a broader time span than just the moment of data collection. Followed accounts are likely to be a less dynamic cue than other cues available on social media (e.g., language used in posts). This is not to say that network ties are unrelated to dynamic states entirely, and that possibility could be explored with different methods. For example, rather than focusing on whether accounts are followed or not, researchers could use engagements with accounts (such as liking or retweeting) to predict momentary reports of mental health symptomatology, or they could track users over time to measure new follows added after an event. The present work can only speak indirectly to these possibilities, but exploring approaches that dynamically link network ties to psychological states is a promising future direction for this work.

The present results, and the possibility of even higher predictive accuracy or greater temporal resolution with more sophisticated methods, raise important questions about privacy. The input to the prediction algorithm developed in this paper – a list of followed accounts – is publicly available for every Twitter user by default, and it is only hidden if a user sets their entire account to “private.” It is unlikely that users have considered how this information could be used to infer their mental health status or other sensitive topics. Indeed, even people who deliberately refrain from self-disclosing about their mental health online may be inadvertently providing information that could be the basis of algorithmic estimates, a possibility highlighted by the often less-than-straightforward accounts that the algorithms appeared to use in their predictions. With time, technological advancement, and research, these predictions might become even more accurate using similarly non-obvious cues in their predictions, though we cannot say how much more. In this way, the present findings are relevant for individuals to make informed decisions about whether and how to use social media. Likewise, they speak to broader issues of ethics, policy, and technology regulation at a systemic level (e.g., Tufekci, 2020). The possibility of a business, government, or other organization putting their considerable resources into using public social media data to construct profiles of users’ mental health may have useful applications in public health research, but it simultaneously raises concerns about how that may be misused. Our results suggest that accuracy is too low for such utopic or dystopic ends presently, but they highlight the possibilities, and the need for in-depth discussions about data, computation, privacy, and ethics.

Predictive Bias

Predictive algorithms can be biased with respect to gender, race, ethnicity, and other demographics, which can create and reinforce social inequality when those algorithms are used to conduct basic research or in applications (Mullainathan, 2019). When we probed for evidence of predictive bias for gender, we found somewhat inconclusive results. There was more of a pattern of bias in the smaller holdout dataset than in the combined data. In the holdout data, women showed higher observed levels of internalizing symptoms (depression, anxiety, and post-traumatic stress) than men with the same model-predicted scores. In the larger combined dataset, only post-traumatic stress showed this effect, and to a much smaller magnitude. Confidence bands in both datasets often ranged from no effect to moderately large effects in one or both directions. All together, we took this as suggestive but inconclusive evidence that the models may have been biased. If the pattern is not spurious, one possible reason may stem from the fact that the sample had more men than women. If men’s and women’s mental health status is associated with which accounts they follow, but the specific accounts vary systematically by gender, then overrepresentation of men in the training data could have resulted in overrepresentation of their followed accounts in the algorithm.

We found little to no evidence of bias with respect to race or ethnicity. The relative lack of bias is initially reassuring, but it should be considered alongside two caveats. First, it is possible that there is some amount of bias that we were unable to detect with the numbers of racial and ethnic minority participants in this dataset. This possibility is highlighted by the confidence bands, which (like gender) tended to range from no effect to moderately large effects. Second, it is possible that collapsing into White vs. non-White is obscuring algorithmic bias that is specific to various racial and ethnic identities. Our decision to combine minority racial and ethnic groups was based on the limitations of the available data, and it necessarily collapses across many substantively important differences.

In any future work to extend or apply the followed-accounts prediction method we present in this study, we strongly encourage researchers to attend carefully to the potential for algorithmic bias. We also hope that this work helps demonstrate how well-established psychometric methods for studying predictive bias can be integrated with modern machine learning methods.

Considering Generalizability At Two Levels of Abstraction

To what extent would the conclusions of this study apply in other settings? There are at least two ways to consider generalizability in this context. The first form of generalizability is a more abstract one, associated with the approach. Would it be possible to obtain similar predictive accuracy by applying this modeling approach to new data drawn from a different population, context, or time, developing a culturally-tuned algorithm for that new setting? We believe the results are likely to be generalizable in this sense. We used cross-validation and out-of-sample testing to safeguard against capitalizing on chance in estimates of accuracy. If the general principle holds that Twitter following decisions are associated with mental health, we expect that it would be possible to create predictive algorithms in a similar way in other settings.

A second, more specific way to think about generalizability is whether the particular prediction algorithms we trained in this study would generalize to entirely new samples from different settings. This is a much higher bar, and we are more skeptical that the models trained in this study would meet it. The fact that the models were not interpretable suggests that they may not have been picking up on theoretically central, universally relevant features of psychopathology. Instead, they might be picking up on real, but potentially fleeting, links between psychopathology and Twitter behavior. By analogy, consider differences between a self-report item like, “I frequently feel sad,” and an item like, “I frequently listen to Joy Division.” The first item would probably be endorsed by depressed people in a wide variety of contexts, populations, and historical eras. The second item, however, is deeply culturally embedded – it if is reflective of depression at all, that association would be highly specific to a particular group of people at a particular cultural moment. Even setting aside that Twitter itself is a product of a specific cultural and historical context, our inspection of the followed accounts suggests that they are not reflecting enduring features of psychopathology in a direct, face-valid sense. The associations with particular accounts were real in this data, but as cultural trends change, they may fade while new ones emerge.

Our results cannot speak to this form of generalizability directly, and it would require a new sample and different design to effectively speak to this. One possibility would be to collect several very different samples (e.g., sampled in different years), train models with each, and then evaluate cross-sample predictive accuracy. This would be a much stricter test of accuracy, but it would provide better justification for using model-derived scores in research or application. Such an approach might also be useful for distinguishing which accounts or features of accounts are predictive because of fleeting cultural factors, and which ones reflect stable and cross-contextually consistent associations with psychopathology.

Stereotypes About Nihilists Are Overwhelmingly Negative; unlike atheists, who are seen as competent, no positive stereotypes emerged for nihilists

Scott, Matthew. 2021. “Stereotypes About Nihilists Are Overwhelmingly Negative.” PsyArXiv. January 26. doi:10.31234/

Abstract: Existential nihilism is on the rise in modern societies, but no previous work has investigated the social psychology of seeing no meaning in life. In the current research, five studies (N = 1,634) show that targets’ existential nihilist beliefs elicit a range of negative stereotypes about personality traits, commonly valued social traits, and targets’ ability to perform basic adaptive social tasks. Results demonstrate that these negative stereotypes are mediated by belief that the target is depressed more than the belief the target is non-religious or that the target does not plan for the future. Unlike atheists, who are seen as competent, no positive stereotypes emerged for nihilists, suggesting both future research and interventions aimed at updating false beliefs about nihilists.

Rolf Degen summarizing... Large-scale study of information behavior in times of coronavirus provides the umpteenth evidence that people do not entrench themselves in echo chambers

COVID-19 Echo Chambers: Examining the Impact of Conservative and Liberal News Sources on Risk Perception and Response. Kenneth A. Lachlan, Emily Hutter, and Christine Gilbert. Health Security, Jan 19 2021.

Rolf Degen's take: Rolf Degen on Twitter: "Large-scale study of information behavior in times of coronavirus provides the umpteenth evidence that people do not entrench themselves in echo chambers."

Abstract: The coronavirus disease 2019 (COVID-19) pandemic has created substantial challenges for public health officials who must communicate pandemic-related risks and recommendations to the public. Their efforts have been further hampered by the politicization of the pandemic, including media outlets that question the seriousness and necessity of protective actions. The availability of highly politicized news from online platforms has led to concerns about the notion of “echo chambers,” whereby users are exposed only to information that conforms to and reinforces their existing beliefs. Using a sample of 5,000 US residents, we explored their information-seeking tendencies, reliance on conservative and liberal online media, risk perceptions, and mitigation behaviors. The results of our study suggest that risk perceptions may vary across preferences for conservative or liberal bias; however, our results do not support differences in the mitigation behavior across patterns of media use. Further, our findings do not support the notion of echo chambers, but rather suggest that people with lower information-seeking behavior may be more strongly influenced by politicized COVID-19 news. Risk estimates converge at higher levels of information seeking, suggesting that high information seekers consume news from sources across the political spectrum. These results are discussed in terms of their theoretical implications for the study of online echo chambers and their practical implications for public health officials and emergency managers.


The findings for Hypothesis 1—that information seeking does not motivate general risk perception—were somewhat unexpected given a lengthy history of research connecting information seeking to perceptions of risk severity. The findings for Hypothesis 2—that information seeking does not motivate mitigation—are also puzzling given a long history of research connecting risk information to protective action. This may be a product of the relative simplicity of CDC guidelines, and the fact that suggestions like wearing a facemask and washing one's hands do not require a great deal of effort. While CDC guidelines have shifted over the course of the pandemic, the individual-level recommendations captured here were fairly consistent during the time data was collected (April through June 2020). The mean across this outcome variable was fairly high for the entire sample (M = 6.11; SD = 1.36 on a 7-point scale), suggesting there may simply have been little variance in the outcome. Hypothesis 3—that information seeking would predict specific estimates of risk—was supported by our analysis. This can perhaps be traced to the underlying processes surrounding information seeking through websites. Whether seeking information from conservative or liberal sources, information seeking requires some degree of active processing. It may be that heavy information seekers, by definition, engage in active processing and are, therefore, better able to encode risks and process them into specific estimates of infection, health risk, and mortality.

Findings for the research question—concerning the moderating effect of reliance on conservative and liberal websites—may shed further light on the findings for all 3 hypotheses and are the most interesting and impactful findings in this study. With regard to echo chambers, our findings for the research question largely indicate that higher information seekers did not experience attitudinal polarization; in fact, across all 3 outcome variables the risk estimates for those reliant on liberal and conservative news content converged at higher levels of information seeking. In other words, lower information seekers, those reliant on conservative sources, reported the lowest levels of risk probability, whereas those reliant on liberal sources reported the highest (Figures 12, and 3). At high levels of information seeking these differences disappear. Accordingly, the impact of information seeking on risk estimates is higher among those reliant on conservative websites, since they have further to go to converge; this is evident in the standardized conditional effects (Tables 12, and 3).

In short, these findings run counter to the notion of echo chambers, and more closely approximate the argument of Messing and Westwood35—that those who engage in high levels of information seeking likely gather information from a range of sources. It may also be the case that high information seekers draw from a range of platforms and may be more open to information that does not align with (or challenges) existing attitudes and beliefs. This would also explain the failure to find a relationship between information seeking and both general risk perception and mitigation (Hypotheses 1 and 2). If high information seekers draw from liberal and conservative news sources, they would likely be exposed to or open to a range of perspectives, including those suggesting high risk and the need to take protective action. This exposure could potentially weaken direct effects between overall information seeking and the variable outcomes.

If politicized underreporting of the threats associated with COVID-19 is a concern, the lower information seekers may be more at risk, as this is where clear differences are evident in risk estimation by source preference. This finding is particularly alarming when considering Slater's25 arguments concerning polarization spirals; if low information seekers with polarized conservative opinions consume congenial information about the pandemic, and only congenial information, they may be likely to double down on their positions concerning specific risk estimates and become even more inclined to seek information that affirms those positions.

Although this is a single study in a highly specified context, health officials may wish to consider these findings when countering misinformation and understatements of risk. The most impressionable audiences may be those who seek the least amount of information and are, therefore, susceptible to information that confirms their biases. Identifying and segmenting these audiences along media preferences and demographic and social strata may enable health officials to target risk messages to those least likely to actively seek information.

People asks for cosmetic surgery because conference video displays, inter alia, one’s emotions in real-time, which may cause a person to notice expression lines and wrinkles which they do not see in the mirror

Zooming into Cosmetic Procedures During the COVID-19 Pandemic: The Provider’s Perspective. Shauna M. Rice et al. International Journal of Women's Dermatology, January 12 2021.

Abstract: The COVID-19 pandemic has seen a massive shift towards virtual living, with video-conferencing now a primary means of communication for both work and social events. Individuals are finding themselves staring at their own video reflection often for hours a day, scrutinizing a distorted image on screen and developing a negative self-perception. This survey study of over 100 board-certified dermatologists across the country elucidates a new problem of “Zoom Dysmorphia” where patients are seeking cosmetic procedures in order to improve their distorted appearance on video-conferencing calls.


Despite the COVID-19 pandemic limiting in-person office visits and stalling many elective procedures, dermatology cosmetic consults are on the rise relative to pre-pandemic times. With people now spending record amounts of time on virtual platforms seeing their virtual image, they are becoming more critical of their features and inquiring about cosmetic improvements. In this survey study of over 100 board certified dermatologists from across the country, over 50% indicated a relative rise in cosmetic consultations within their practices despite the state of the pandemic (Figure 1). Even more notable is that 86% of respondents report their patients are referencing video-conferencing as a reason for their new cosmetic concerns (Figure 2). Other studies have noted similar results, with one recent survey of the general public showing that of those who previously did not have an interest in facial cosmetic treatments, 40% now plan to pursue treatments based on concerns from their video-conferencing appearance alone (Cristel et al, 2020).


According to surveyed dermatologists, neuromodulating agents such as Botox and Dysport, filler injections, and laser treatments were noted as the most frequently requested cosmetic procedures reaching their offices. In a time where more invasive aesthetic surgical procedures are restricted for concern of unnecessary virus spread, higher interest in minimally invasive procedures is expected. Patients appear to be the most concerned with regions from the neck up, most notably the forehead/glabella, eyes, neck, and hair. Specific concerns include upper face wrinkles, circles/bags under the eyes, dark spots, and neck sagging (Figure 3). Concerns below the neck were much less frequently reported, with body contouring and cellulite treatments noted to be on the rise by less than 10% of surveyed dermatologists. Interestingly, an analysis of Google search trends during the COVID-19 pandemic showed an increase in search terms such as “acne” and “hair loss” (Kutlu, 2020). Authors of that analysis attributed the search trend to the association of acne and hair loss with anxiety and depression, psychological conditions weighing heavily on many quarantined individuals. Numerous other factors may also play a role, such as mask-occlusion causing acne mechanica as well as the association of telogen effluvium with COVID-19 infection (add citation). Our results show the trend may also be due to the fact that people are now becoming more aware of their appearance, scrutinizing their features from the neck up as they see their video reflection daily.


Of concern to providers is that patients are requesting more procedures as a result of increased video-conferencing, which has been shown in the literature to reflect a distorted facial appearance. This is causing concern for aspects of appearance that may not truly require correction or to the extent that the patient fears. Of even greater concern is the mental health aspect that is uncovered in this study, that 82.7% of surveyed providers report their patients feeling more displeased with their appearance now than ever before (Figure 4). The psychological response to the COVID-19 pandemic has been understandably negative, but why are patients so unhappy particularly with their appearance on Zoom? Studies have shown that those with higher levels of engagement on social media have higher levels of body dissatisfaction and depression (Shome et al., 2020Woods and Scott, 2016). For example, one study showed that when instructed to upload photos to social media, most participants noted a decrease in self-confidence and an increase in desire to undergo cosmetic surgery (Shome et al,2020). Although Zoom may not be considered social media, it does necessitate that people expose themselves in a virtual manner for which many are unaccustomed. Zoom adds an additional level of complexity by displaying one’s emotions in real-time, leading us to watch ourselves speak and react to others, which may cause a person to notice expression lines and wrinkles which they are not used to seeing while looking in the mirror. Additionally, one’s reflection is displayed side by side to other members of the call, allowing for easy comparison and self-judgment.


The distorting effects of webcams could also contribute to the observed trend in cosmetic consults, as patients remain unaware of how cameras can distort and degrade video quality and inaccurately represent one’s true appearance. For instance, camera angle and focal distance make a difference in the image that appears on screen. A 2018 study found that a portrait taken from 12 inches away increases perceived nose size by about 30% when compared to an image taken at 5 feet (Ward et al, 2018). With webcams often recording at shorter focal lengths, the result is an overall more rounded face, wider set eyes, broader nose, taller forehead and disappearing ears obscured by cheeks (Trebicky et al, 2016). Moreover, video calls condense life into a 2D image, leading a graded shadow along a curved surface such as the nose to appear as a flat, darkened area instead (Lu and Bartlett, 2016). This illusion may exacerbate the appearance of facial dark spots and bring unnecessary concern to users.

Combining these elements with the current trends in cosmetic consults, it is apparent that Zoom, although a useful and necessary tool for maintaining productivity during quarantine, has introduced individuals to an unfamiliar virtual environment. This increased self-exposure and distorted image on screen may be causing patients to develop thoughts of BDD, with a tendency to be preoccupied with a real or imagined physical defects and causing functional impairment. These patients often seek cosmetic procedures to improve their perceived appearance, yet are rarely satisfied with the results, ending up in a cycle of self-dissatisfaction. Approximately 9-14% of patients in general dermatology clinics have a diagnosis of BDD, and within the cosmetic surgery setting, the prevalence is thought to be even higher (Vashi, 2016). With anxiety disorders on the rise due to factors related to the pandemic, BDD is an important consideration in patient evaluations. Elucidating the limitations of webcams and examining the trends of this new virtual world, we can better serve our patients by screening for such dysmorphic thoughts and connecting patients with appropriate counseling. Prior to the pandemic, patients presented to their aesthetic physicians hoping to look more like their filtered Snapchat selfies; we have now entered an era in which people are forced to confront a distorted and often unflattering rendition of themselves for hours a day on Zoom, distorted reflection, promoting the phenomenon of “Zoom Dysmorphia.”

We found that surprisingly, children as young as age 4 viewed morally bad people as less happy than morally good people, even if the characters all have positive subjective states

Yang, F., Knobe, J., & Dunham, Y. (2021). Happiness is from the soul: The nature and origins of our happiness concept. Journal of Experimental Psychology: General, 150(2), 276–288, Jan 2021.

Rolf Degen's take: Rolf Degen on Twitter: "Even at young age, children proceed from the assumption that bad people are not happy."

Abstract: What is happiness? Is happiness about feeling good or about being good? Across 5 studies, we explored the nature and origins of our happiness concept developmentally and cross-linguistically. We found that surprisingly, children as young as age 4 viewed morally bad people as less happy than morally good people, even if the characters all have positive subjective states (Study 1). Moral character did not affect attributions of physical traits (Study 2) and was more powerfully weighted than subjective states in attributions of happiness (Study 3). Moreover, moral character but not intelligence influenced children and adults’ happiness attributions (Study 4). Finally, Chinese people responded similarly when attributing happiness with 2 words, despite one (“Gao Xing”) being substantially more descriptive than the other (“Kuai Le”) (Study 5). Therefore, we found that moral judgment plays a relatively unique role in happiness attributions, which is surprisingly early emerging and largely independent of linguistic and cultural influences, and thus likely reflects a fundamental cognitive feature of the mind.