Real-world Events Research Articles

The Effect of a 3-Minute Mindfulness Intervention, and the Mediating Role of Maximization, on Critical Incident Decision-Making

Objective In this study, we extend the impact of mindfulness to the concept of least-worst decision-making. Least-worst decisions involve high-uncertainty and require the individual to choose between a number of potentially negative courses of action. Research is increasingly exploring least-worst decisions, and real-world events (such as the COVID-19 pandemic) show the need for individuals to overcome uncertainty and commit to a least-worst course of action. From sports to business, researchers are increasingly showing that being mindful has a range of positive performance-related benefits. We hypothesized that mindfulness would improve least-worst decision-making because it would increase self-reflection and value identification. However, we also hypothesized that trait maximization (the tendency to attempt to choose the best course of action) would negatively interact with mindfulness. Methods Three hundred and ninety-eight participants were recruited using Amazon MTurk and exposed to a brief mindfulness intervention or a control intervention (listening to an audiobook). After this intervention, participants completed the Least-Worst Uncertain Choice Inventory for Emergency Responders (LUCIFER). Results As hypothesized, mindfulness increased decision-making speed and approach-tendencies. Conversely, for high-maximizers, increased mindfulness caused a slowing of the decision-making process and led to more avoidant choices. Conclusions This study shows the potential positive and negative consequences of mindfulness for least-worst decision-making, emphasizing the critical importance of individual differences when considering both the effect of mindfulness and interventions aimed at improving decision-making.

Mining and Validating Social Media Data for COVID-19-Related Human Behaviors Between January and July 2020: Infodemiology Study.

BackgroundHealth authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak.ObjectiveWe used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications.MethodsWe developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here.ResultsWe applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States.ConclusionsBehaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

Learning Scheduling Models from Event Data

A significant challenge in declarative approaches to scheduling is the creation of a model: the set of resources and their capacities and the types of activities and their temporal and resource requirements. In practice, such models are developed manually by skilled consultants and used repeatedly to solve different problem instances. For example, in a factory, the model may be used each day to schedule the current customer orders. In this work, we aim to automate the creation of such models by learning them from event data. We introduce a novel methodology that combines process mining, timed Petri nets (TPNs), and constraint programming (CP). The approach learns a sub-class of TPN from event logs of executions of past schedules and maps the TPN to a broad class of scheduling problems. We show how any problem of the scheduling class can be converted to a CP model. With new instance data (e.g., the day’s orders), the CP model can then be solved by an off-the-shelf solver. Our approach provides an end-to-end solution, going from event logs to model-based optimal schedules. To demonstrate the value of the methodology we conduct experiments in which we learn and solve scheduling models from two types of data: logs generated from job-shop scheduling benchmarks and real-world event logs from an outpatient hospital.

Real-world clinical adverse events (AEs) among nonmetastatic castration-resistant prostate cancer (nmCRPC) patients treated with novel anti-androgen (AA) therapies: A retrospective database study.

e18675 Background: Novel AA agents, such aspalutamide (Apa), enzalutamide (Enza) and darolutamide (Daro) are approved for the treatment of nmCRPC. In clinical trials, they were associated with AEs such as fatigue, fractures and falls. This study aimed to describe the real-world incidence of AEs among nmCRPC patients who initiated treatment with AA agents. Methods: Claims from Optum Clinformatics Data Mart, a large single-payer claims database were used to retrospectively identify adult males with prostate cancer (PC), evidence of castration, absence of metastases codes and >1 claim for AA therapies Apa and Enza (index date) from Jan 2017 through Dec 2019. Daro, a more recently approved AA, was not included due to lack of data. Incidence of central nervous system (CNS) (including but not limited to fatigue, falls and cognitive impairment) and any AE (fracture, rash, hypertension among others) were estimated and Kaplan-Meier analyses were used to quantify the median time from therapy initiation to clinical event occurrence. Multivariable Cox-proportional hazards model was used to identify clinical event predictors. A washout of previous AE events in the 1-year baseline was applied to identify true incident events. Results: A total of 605 patients (156 Apa and 449 Enza) were included in the study. The majority were ≥65 yrs (94.0%), white (56.0%), resided in the south (47.1%), and had Medicare insurance (86.4%). Almost all were medically castrated (99.0%) and mean Charlson Comorbidity Index (CCI) Score was 2.28. Incidence rates of all clinical events and CNS-related events were high (71.2% and 46.1%, respectively) with no major differences between Apa and Enza (Table). The median time from Apa or Enza treatment initiation to any clinical event was 71 and 64 days, respectively, and 282 and 209 days for CNS-related events, respectively. Significant predictors of having any events included age >75 yrs and high CCI score. Similar predictors were found for any CNS events. Conclusions: More than 70% of Apa and Enza treated nmCRPC patients had at least 1 clinical AE. Given the lack of a control cohort, further study is needed to assess whether these events are related to AAs or represent inherent risks of these events in the underlying population of largely older men with advanced PC. Future studies are warranted with all approved agents including Daro. [Table: see text]

Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling

Event-based corporate profiling aims to assess the evolving operational status of the corresponding corporate from its event sequence. Existing studies on corporate profiling have partially addressed the problem via (i) case-by-case empirical analysis by leveraging traditional financial methods, or (ii) the automatic profile inference by reformulating the problem into a supervised learning task. However, both approaches heavily rely on domain knowledge and are labor-intensive. More importantly, the task-specific nature of both approaches prevents the obtained corporate profiles from being applied to diversified downstream applications. To this end, in this paper, we propose a Self-Supervised Prototype Representation Learning (SePaL) framework for dynamic corporate profiling. By exploiting the topological information of an event graph and exploring self-supervised learning techniques, SePaL can obtain unified corporate representations that are robust to event noises and can be easily fine-tuned to benefit various down-stream applications with only a few annotated data. Specifically, we first infer the initial cluster distribution of noise-resistant event prototypes based on latent representations of events. Then, we construct four permutation-invariant self-supervision signals to guide the representation learning of the event prototype. In terms of applications, we exploit the learned time-evolving corporate representations for both stock price spike prediction and corporate default risk evaluation. Experimental results on two real-world corporate event datasets demonstrate the effectiveness of SePaL for these two applications.

Method for Viewing Real-World Scenes while Recording Video

Because smartphones support various functions, they are carried by users everywhere. Whenever a user believes that a moment is interesting, important, or meaningful to them, they can record a video to preserve such memories. The main problem with video recording an important moment is the fact that the user needs to look at the scene through the mobile phone screen rather than seeing the actual real-world event. This occurs owing to uncertainty the user might feel when recording the video. For example, the user might not be sure if the recording is of high-quality and might worry about missing the target object. To overcome this, we developed a new camera application that utilizes two main algorithms, the minimum output sum of squared error and the histograms of oriented gradient algorithms, to track the target object and recognize the direction of the user’s head. We assumed that the functions of the new camera application can solve the user’s anxiety while recording a video. To test the effectiveness of the proposed application, we conducted a case study and measured the emotional responses of users and the error rates based on a comparison with the use of a regular camera application. The results indicate that the new camera application induces greater feelings of pleasure, excitement, and independence than a regular camera application. Furthermore, it effectively reduces the error rates during video recording.

The Fallacy of an Airtight Alibi: Understanding Human Memory for “Where” Using Experience Sampling

A primary challenge for alibi-generation research is establishing the ground truth of real-world events of interest. In the current study, we used a smartphone app to record data on adult participants (N = 51) for a month prior to a memory test. The app captured accelerometry data, GPS locations, and audio environments every 10 min. After a week-long retention interval, we asked participants to identify where they were at a given time from among four alternatives. Participants were incorrect 36% of the time. Furthermore, our forced-choice procedure allowed us to conduct a conditional logit analysis to assess the different aspects of the events that the participants experienced and their relative importance to the decision process. We found strong evidence that participants confuse days across weeks. In addition, people often confused weeks in general and also hours across days. Similarity of location induced more errors than similarity of audio environments or movement types.

CASE STUDY APPROACH TO TEACHING AND LEARNING ENGLISH FOR MEDICAL PURPOSES

The expression case study implies a wide range of issues presented for analysis, based on actual or simulated events that could logically occur. Besides, it involves profound research of phenomena or events. In medical education and practice, a case study represents an essential diagnostic procedure and investigation method used to create a profound understanding of an intricate problem in its real context. The implementation of the case study approach is of great importance in teaching English for medical purposes because the medical topics presented to students are familiar to them and medical terminology is applied to language learning. The case study is so structured as to serve the communicative function and provides the necessary content. It comprises the most relevant aspects in the field of medicine such as presenting signs and symptoms, performing different types of examinations, treatment, and health care management. The standard problem that needs to be solved is the patient’s problem, and establishing an adequate diagnosis is the goal. In this way, students develop critical thinking and reflective learning, improve organizational skills, as well as the ability to see the complexity of real-world events and understand the perspective of others. This method has proven to be highly practical preparing students for reality, not theory workplace. In this way integration of language and the professional setting is enabled.

Discovering and Analyzing Contextual Behavioral Patterns From Event Logs

Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process’ behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. A complete analysis methodology leveraging all the tools presented in the paper and supplemented by interpretations guidelines is also provided. Finally, experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights.

The Effects of Negative Online Reviews on Consumer Perception, Attitude and Purchase Intention: Experimental Investigation of the Amount, Quality, and Presentation Order of eWOM

The quick growth and fast spread of electronic word-of-mouth (eWOM) have created a new threat to Internet merchants and marketers through paid online reviewers flooding sites with product and service reviews that could confuse and deter customers. This study examined the effects of the posts by paid reviewers—specifically, the negative reviews—on consumers’ risk perception, product attitude, and purchase intention. While extant research examined negative eWOM as an information source, little attention has been paid to the role of a hired reviewer's post aimed at destroying the reputation of certain targets (Internet Water Army Attack [IWAA]). To gain a better understanding of this phenomenon, three experiments were conducted to investigate the effects of the amount, quality, and presentation order of negative eWOM on consumers’ perception change and decision making. We tested the hypothesis using a test environment that mimicked a PTT online forum ( https://www.ptt.cc/ ) in Taiwan. Three simulation cases (smartphone, digital camera, and tablet) based on real-world events were used. A total of 193 participants completed all three experiments and provided valid responses. The results of this study are mostly consistent with previous research findings that online marketing is greatly threatened by negative eWOM. Nevertheless, it was also found that the effects of the amount, quality, and presentation order of negative eWOM are more complicated than we have anticipated. The findings revealed that IWAA can effectively increase customers’ risk perception toward a product and change their attitude and purchase intention.

Examining the Left-Right Divide Through the Lens of a Global Crisis: Ideological Differences and Their Implications for Responses to the COVID-19 Pandemic.

The COVID‐19 disease pandemic is one of the most pressing global health issues of our time. Nevertheless, responses to the pandemic exhibit a stark ideological divide, with political conservatives (versus liberals/progressives) expressing less concern about the virus and less behavioral compliance with efforts to combat it. Drawing from decades of research on the psychological underpinnings of ideology, in four studies (total N = 4441) we examine the factors that contribute to the ideological gap in pandemic response—across domains including personality (e.g., empathic concern), attitudes (e.g., trust in science), information (e.g., COVID‐19 knowledge), vulnerability (e.g., preexisting medical conditions), demographics (e.g., education, income) and environment (e.g., local COVID‐19 infection rates). This work provides insight into the most proximal drivers of this ideological divide and also helps fill a long‐standing theoretical and empirical gap regarding how these various ideological differences shape responses to complex real‐world sociopolitical events. Among our key findings are the central role of attitude‐ and belief‐related factors (e.g., trust in science and trust in Trump)—and the relatively weaker influence of several domain‐general personality factors (empathic concern, disgust sensitivity, conspiratorial ideation). We conclude by considering possible explanations for these findings and their broader implications for our understanding of political ideology. Highlights Stark ideological differences exist across a wide range of attitudinal and behavioral indices of pandemic response, with more conservative individuals reliably exhibiting less concern about the virus. These findings illustrate the extent to which the pandemic has become politicized.A range of factors contribute to this ideological gap in pandemic response, but some are substantially more important than others.Several factors that have received attention in public and academic discourse about the pandemic appear to contribute little, if at all, to the ideological divide. These include news following, scientific literacy, perceived social norms, and knowledge about the virus.The most critical factors appear to be trust in scientists and trust in Trump, which further highlights the politicization of COVID‐19 and, importantly, the antagonistic nature of these two beliefs. Efforts to change and, especially, disentangle these two attitudes have the potential to be effective interventions.

Temporally sorting images from real-world events

• A data-driven method for chronologically sorting images of real-world events by analyzing the appearance of the scene. • The duration of the event is split into successive intervals and their relationships are modeled hierarchically. • An evaluation of several data augmentation techniques in scenarios with limited data availability. • A visualization of the concepts learned by the model to sort images offers additional insights about the problem. As smartphones become ubiquitous in modern life, every major event — from musical concerts to terrorist attempts — is massively captured by multiple devices and instantly uploaded to the Internet. Once shared through social media, the chronological order between available media pieces cannot be reliably recovered, hindering the understanding and reconstruction of that event. In this work, we propose data-driven methods for temporally sorting images originated from heterogeneous sources and captured from distinct angles, viewpoints, and moments. We model the chronological sorting task as an ensemble of binary classifiers whose answers are combined hierarchically to estimate an image’s temporal position within the duration of the event. We evaluate our method on images from the Notre-Dame Catedral fire and the Grenfell Tower fire events and discuss research challenges for analyzing data from real-world forensic events. Finally, we employ visualization techniques to understand what our models have learned, offering additional insights to the problem.

IEEE Internet Computing: Special Issue on the Effects of Social Media on Real-World Events

IEEE Internet Computing: Special Issue on the Effects of Social Media on Real-World Events

Real-world hematological adverse events in Chinese patients with advanced ovarian cancer treated with an individualized starting dose of niraparib.

BackgroundThis work set out to examine the hematological adverse events (AEs) of an individualized starting dose (ISD) of niraparib in Chinese patients with ovarian cancer (OC).MethodsThe medical records of 43 patients with OC who were treated with an ISD of niraparib at the Cancer Hospital of The University of Chinese Academy of Sciences between February 2019 and January 2020 were retrospectively reviewed. Treatment-emergent hematological AEs were analyzed.ResultsOf the 43 patients with OC, 28 (65.1%) had hematological AEs of ≥ grade 1, including thrombocytopenia (39.5%), leukopenia (37.2%), and anemia (34.9%). Ten (23.3%) patients developed grade 3/4 hematological AEs, including thrombocytopenia (11.6%), leukopenia (9.3%), and anemia (7.0%). Among the individuals who developed AEs during treatment, 9 (32.1%) patients had their treatment interrupted, with treatment being restarted in 8 (28.6%) cases, and 4 (14.3%) patients had the drug dose decreased. No deaths were reported. The median times to the occurrence of any-grade leukopenia, anemia, and thrombocytopenia were 30 (range, 7 to 162), 34 (range, 7 to 108), and 20 (range, 13 to 180) days, respectively. Most AEs occurred within the first 3 months of treatment (93.8% leukopenia, 80.0% anemia, and 76.5% thrombocytopenia). Treatments for AEs included supplementation of recombinant human granulocyte colony-stimulating factor (n=5, 17.9%), erythrocytes (n=2, 7.1%), and recombinant human thrombopoietin (n=5, 17.9%).ConclusionsThe incidence of adverse hematological reactions to an ISD of niraparib in Chinese patients with advanced OC is relatively lower in the real world than in the phase III clinical trials PRIMA (also an ISD) and NOVA. These hematological AEs can be managed through dose adjustment and symptomatic therapy.

A Geometry-Driven Longitudal Topic Model

A simple and scalable framework for longitudinal analysis of Twitter data is developed that combines latent topic models with computational geometric methods. Dimensionality reduction tools from computational geometry are applied to learn the intrinsic manifold on which the latent, temporal topics reside. Then shortest path distances on the manifold are used to link together these topics. The proposed framework permits visualization of the low-dimensional embedding, which provides clear interpretation of the complex, high-dimensional trajectories that may exist among latent topics. Practical application of the proposed framework is demonstrated through its ability to capture and effectively visualize natural progression of latent COVID-19–related topics learned from Twitter data. Interpretability of the trajectories is achieved by comparing to real-world events. In addition, the framework permits study of spatial variation in Twitter behavior for learned topics. The analysis demonstrates that the proposed framework is able to capture granular-level impact of COVID-19 on public discussions. We end by arguing that Twitter data, when analyzed within the proposed framework, can serve as a valuable supplementary data stream for COVID-related studies.

Turning value into action: Healthcare workers using digital media advocacy to drive change.

BackgroundThe standard method of sharing information in academia is the scientific journal. Yet health advocacy requires alternative methods to reach key stakeholders to drive change. The purpose of this study was to analyze the impact of social media and public narrative for advocacy in matters of firearm-related injury and death.Study designThe movement This Is Our Lane was evaluated through the #ThisIsOurLane and #ThisIsMyLane hashtags. Sources were assessed from November 2018 through March 2019. Analyses specifically examined message volume, time course, global engagement, and content across Twitter, scientific literature, and mass media. Twitter data were analyzed via Symplur Signals. Scientific literature reviews were performed using PubMed, EMBASE, Web of Science, and Google Scholar. Mass media was compiled using Access World News/Newsbank, Newspaper Source, and Google.ResultsA total of 507,813 tweets were shared using #ThisIsOurLane, #ThisIsMyLane, or both (co-occurrence 21–39%). Fifteen scientific items and n = 358 mass media publications were published during the study period; the latter included articles, blogs, television interviews, petitions, press releases, and audio interviews/podcasts. Peak messaging appeared first on Twitter on November 10th, followed by mass media on November 12th and 20th, and scientific publications during December.ConclusionsSocial media enables clinicians to quickly disseminate information about a complex public health issue like firearms to the mainstream media, scientific community, and general public alike. Humanized data resonates with people and has the ability to transcend the barriers of language, culture, and geography. Showing society the reality of caring for firearm-related injuries through healthcare worker stories via digital media appears to be effective in shaping the public agenda and influencing real-world events.

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning.

The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities.

Deep memory distortions

Deep distortions are a new family of memory biases that comprise one of the two basic varieties of false memory. The first and older variety, surface distortions, are specific item or source memories that are erroneous because the events did not happen. The new variety, deep distortions, are emergent properties of multiple specific memories. They are relations among such memories that are false because they violate objective logical rules that real-world events must obey. I discuss four deep distortions for which substantial data have accumulated: overdistribution, super-overdistribution, non-additivity, and impossible conjunctions. These phenomena violate four axioms of classical probability (numerical bound, universal event, additivity, and countable additivity) and two rules that follow from them (empty set and monotonicity). Their psychological significance lies in four facts about them: (a) They demonstrate that although events in the real world are compensatory, our memories of them are not; (b) they establish that we persistently over remember experience; (c) they reveal that surface distortions are by-products of deep distortions; and (d) they pose the theoretical conundrum of how the structure of memory could so thoroughly misrepresent the objective structure of the events we are remembering.

"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study.

BackgroundThe COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts.ObjectiveThe aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic.MethodsWe began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time.ResultsAnalysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events.ConclusionsAlthough we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.

Friendship jealousy: One tool for maintaining friendships in the face of third-party threats?

Friendships can foster happiness, health, and reproductive fitness. However, friendships end-even when we might not want them to. A primary reason for this is interference from third parties. Yet, little work has explored how people meet the challenge of maintaining friendships in the face of real or perceived threats from third parties, as when our friends inevitably make new friends or form new romantic relationships. In contrast to earlier conceptualizations from developmental research, which viewed friendship jealousy as solely maladaptive, we propose that friendship jealousy is one overlooked tool of friendship maintenance. We derive and test-via a series of 11 studies (N = 2,918) using hypothetical scenarios, recalled real-world events, and manipulation of online emotional experiences-whether friendship jealousy possesses the features of a tool well-designed to help us retain friends in the face of third-party threats. Consistent with our proposition, findings suggest that friendship jealousy is (a) uniquely evoked by third-party threats to friendships (but not the prospective loss of the friendship alone), (b) sensitive to the value of the threatened friendship, (c) strongly calibrated to cues that one is being replaced, even over more intuitive cues (e.g., the amount of time a friend and interloper spend together), and (d) ultimately motivates behavior aimed at countering third-party threats to friendship ("friend guarding"). Even as friendship jealousy may be negative to experience, it may include features designed for beneficial-and arguably prosocial-ends: to help maintain friendships. (PsycInfo Database Record (c) 2021 APA, all rights reserved).