Emotional framing in online environmental activism: Pairing a Twitter study with an offline experiment

As the consequences of anthropogenic climate change become more apparent, social media has become a central tool for climate change activists to raise awareness and to mobilise society. In two studies, we examine which features of activist messages affect their popularity and behavioural intentions towards climate action. In the first study, tweets (N = 510k) of fifty climate activists posted between November 2015 and December 2020 are examined to measure their emotional content and its relation to tweet diffusion. Climate-related tweets are found to be shared more the less they contain positive emotion and the more they contain negative emotion. This result supports the negativity bias in climate communication on social media. In Study 2 (N = 200), we experimentally test whether negatively versus positively framed environmental content leads to increased reported intent to engage with collective action, and whether mood mediates that link. We find both direct and indirect effects on reported climate action intentions when mood is used as a mediator. The negative mood resulting from seeing negative tweets makes participants more likely to report higher action intention (indirect effect) - congruent with Study 1. However, seeing negative tweets also makes participants less inclined to act (direct effect), indicating a suppression effect and the presence of other factors at work on the pathway between information and action intent formation. This work highlights the complex and multifaceted nature of this relation and motivates more experimental work to identify other relevant factors, as well as how they relate to one another

Left out – Feelings of social exclusion incite individuals with high conspiracy mentality to reject complex scientific messages

We investigated linguistic factors that affect peoples’ trust in science and their commitment to follow evidence-based recommendations, crucial for limiting the spread of COVID-19. In an experiment (N = 617), we examined whether complex (vs. simple) scientific statements on mask-wearing can decrease trust in information and its sources, and hinder adherence to behavioral measures. In line with former research on social exclusion through complex language, we also examined whether complexity effects are mediated via feelings of exclusion. Results indicate that negative effects of text complexity were present, but only for participants with a strong conspiracy mentality. This finding informs how to decrease distrust in science among individuals with high conspiracy mentality, a population commonly known for its rejection of scientific evidence

Gender Bias in Special Issues: Evidence from a Bibliometric Analysis

Even though the majority of psychologists are women, they are outnumbered by men in senior academic ranks. One reason for this representation bias in academia is that men favor other men in decision-making, especially when the stakes are high. We tested the possibility of such bias in a bibliometric analysis, in which we coded editors' and authors' gender in regular and special issues, the latter considered of higher scientific prominence. We examined all special issues from five prominent scientific outlets in the fields of personality and social psychology published in the 21st century. Altogether, we analyzed 1,912 articles nested in 93 sets comprising a special issue and a neighboring regular issue treated as a control condition. For articles published in special (but not regular) issues, when there were more men editors, more men first-authored and co-authored the work. This pattern suggests how gender bias can be perpetuated within academia and calls for revising the editorial policies of leading psychology journals

BERTAgent: The Development of a Novel Tool to Quantify Agency in Textual Data

Agency, pertaining to goal-orientation and achievement, is a fundamental aspect of human cognition and behavior. Accordingly, detecting and quantifying linguistic representations of agency is critical for the analysis of human actions, interactions, and social dynamics. Available agency-quantifying computational tools rely on word-counting methods, which are insensitive to the semantic context in which the words are used and consequently are inaccurate in case of polysemy and negation. Additionally, some currently available tools fail to account for differences in the intensity and directionality (valence) of agency. In order to overcome these shortcomings, we present BERTAgent, a novel tool to quantify semantic agency in text. BERTAgent is a computational language model that utilizes the transformers architecture, a popular deep learning approach to natural language processing. BERTAgent was fine-tuned using carefully selected textual data that were evaluated by human coders with respect to the level of conveyed agency. In five validation studies, we demonstrate that BERTAgent outperforms previous solutions in terms of convergent and discriminant validity. Additionally, the detailed description of BERTAgent’s development procedure serves as a tutorial for the advancement of similar tools, providing a blueprint for leveraging the existing lexicographical datasets in conjunction with the deep learning techniques in order to detect and quantify other psychological constructs in textual data. https://pypi.org/project/bertagent/ https://bertagent.readthedocs.io/ https://github.com/cogsys-io/BERTAgent-SOM/ https://github.com/cogsys-io/bertagent

Data_Sheet_1_Emotional framing in online environmental activism: Pairing a Twitter study with an offline experiment.PDF

As the consequences of anthropogenic climate change become more apparent, social media has become a central tool for environmental activists to raise awareness and to mobilize society. In two studies, we examine how the emotional framing of messages posted by environmental activists influences engagement and behavioral intentions toward environmental action. In the first study, tweets (N = 510k) of 50 environmental activists posted between November 2015 and December 2020 are examined to measure their emotional content and its relation to tweet diffusion. Environment-related tweets are found to be shared more the less they contain positive emotion and the more they contain negative emotion. This result supports the negativity bias on social media. In Study 2 (N = 200), we experimentally test whether negatively vs. positively framed environmental content leads to increased reported intent to engage with collective action, and whether mood mediates that link. We find both direct and indirect effects on reported climate action intentions when mood is used as a mediator. The negative mood resulting from seeing negative tweets makes participants more likely to report higher action intention (indirect effect)—congruent with Study 1. However, seeing negative tweets also makes participants less inclined to act (direct effect), indicating a suppression effect and the presence of other factors at work on the pathway between information and action intent formation. This work highlights the complex and multifaceted nature of this relation and motivates more experimental work to identify other relevant factors, as well as how they relate to one another.</p

Mobilize is a Verb

In three studies, we investigated the role of linguistic features characterizing texts aiming to mobilize others. In Study 1 (N = 728), participants produced a leaflet either mobilizing others to engage in an action or expressing their thoughts about that action, and evaluated how action-oriented their text was. Mobilizing texts included more verbs and concrete words, and the presence of these linguistic characteristics was positively linked to participants’ evaluations of their messages as action-oriented. In Studies 2 and 3 (N = 557 and N = 556), independent groups of participants evaluated texts produced in Study 1. Readers’ perceptions of texts as action-oriented were associated with the same linguistic features as in Study 1 and further positively linked to perceived message effectiveness (Study 2) and behavioral intention (Study 3). The studies reveal how encoding and decoding of verbs and concrete words serve as distinct persuasive tools in calls to action

Riot like a Girl? Gender-Stereotypical Associations Boost Support for Feminist Online Campaigns

Women face negative consequences for violating traditional gender stereotypes. In this research, we demonstrate that such regulatory function of stereotypes might extend to feminist campaigns that are seen as violating social expectations towards women. In Study 1 (Ntweets = 510,000), we investigated how two real-life movements: #metoo and #sexstrike, are viewed in terms of adhering to the traditional female stereotype of high warmth/communion and low competence/agency, as per language used in the tweets posted within each campaign. We found that the more popular movement, i.e., #metoo, was characterised by the use of more communal and less agentic content than the less popular movement, i.e., #sexstrike. In Study 2 (N = 195), we presented participants with descriptions of bogus movements identical to #metoo and #sexstrike and asked about their associations with and support for the campaigns. It was found that participants associated the #metoo-like campaign with more ‘feminine’ (e.g., community orientation) and less ‘masculine’ concepts (e.g., rebelliousness), which, in turn, translated to a greater declared support for this campaign. In Study 3 (N = 446), conducted within a more controlled and context-independent setting, we once again observed the link between stereotype-congruence of feminist movements and the support they receive. We also found that the effect of stereotypicality was independent of the effect of action normativity. Controlling for feminist identification, political conservatism, and gender system justification did not affect the pattern of results. Uncovering these stereotype-driven mechanisms of support for feminist movements equips women with important knowledge about the costs and benefits of strategies they might use in fight for gender equality