Home team's advantage reduced without crowd support after the COVID-19 outbreak

Historically, fan attendance at sporting events has always been considered an advantage for the home team. The level of fan attendance at soccer matches was evaluated as a decisive effect to modify the performance of the home teams in the most important soccer leagues around the world. We exploit the effects of the absence of spectators driven by COVID-19 pandemic mitigation policies. We find that the advantage of the home field, measured by the proportion of wins and also by the numbers of goals and points scored, is remarkable in many leagues. We performed comparative hypothesis tests with eight of the world's top soccer leagues, confirming a decrease in the probability of a home win, greatly affecting the matches´ outcomes. [ FROM AUTHOR] Copyright of Soccer & Society is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Politics of Intersecting Crises: The Effect of the COVID-19 Pandemic on Climate Policy Preferences

Few contemporary crises have reshaped public policy as dramatically as the COVID-19 pandemic. In its shadow, policymakers have debated whether other pressing crises—including climate change—should be integrated into COVID-19 policy responses. Public support for such an approach is unclear: the COVID-19 crisis might eclipse public concern for other policy problems, or complementarities between COVID-19 and other issues could boost support for broad government interventions. In this research note, we use a conjoint experiment, panel study, and framing experiment to assess the substitutability or complementarity of COVID-19 and climate change among US and Canadian publics. We find no evidence that the COVID-19 crisis crowds out public concern about the climate crisis. Instead, we find that the publics in both countries prefer that their governments integrate climate action into COVID-19 responses. We also find evidence that analogizing climate change with COVID-19 may increase concern about climate change.

Estimating the effect of attendance on home advantage in the National Basketball Association

Using COVID-19 safety protocols as a natural experiment, we are able to delineate three distinct attendance categories in the NBA: 1) unrestricted games played prior to the pandemic, 2) attendance-restricted games played with socially distanced fans, and 3) ‘ghost games' played without fans. Further, since attendance at restricted games was exogenously determined by local COVID-19 protocols that were in turn driven by changes in COVID-19 case counts, we are able to estimate whether the ‘marginal fan' contributes to home advantage. Taken together, our results indicate that the presence of fans matters to home team performance;in fact, ‘ghost games' eliminated home advantage in totality. With a relatively small number of socially distanced fans, however, the entirety of home advantage was retained. Interestingly, since the size of socially distanced crowds had a statistically insignificant impact on home advantage, we find no evidence of a ‘marginal fan' effect. Finally, since researchers have found that officiating is influenced by fans in international soccer (e.g. Anders and Rotthoff, 2014), we explore whether NBA officiating behaviour was altered due to changes in attendance conditions. Our results indicate that NBA officials were not measurably influenced by the presence or quantity of fans.

When the Stadium Goes Silent: How Crowds Affect the Performance of Discriminated Groups

Using a natural experiment induced by COVID-19, we test how the sudden absence of fans at football games impacts player performance in Italy. We find that African players, who are most commonly targeted by racial harassment, play better when fans are no longer at the stadium. A similar, albeit weaker, effect is detected among black players. Using official records of racist behavior by fans, we show that performance improves the most on teams that were subject to abuse before the lockdown. Our evidence suggests that racist pressure can harm discriminated groups and lower the overall quality of the game.

False Start? An Analysis of NFL Penalties With and Without Crowds

The lack of crowds at sports fixtures as a result of COVID-19 restrictions has allowed researchers a unique opportunity to examine the widely accepted convention of home advantage. This work takes a slightly different approach, by examining within game outcomes. Specifically using play-by-play data from the NFL, this paper asks whether the lack of crowds aided a Quarterback's ability to manipulate opposition defenses. Results suggest this was the case, though effects are not uniform by home and away team.

Femitags for feminist connected crowds in Latin America and Spain.

In recent years, women are acting all over the world against gender violence and femicide. This new wave of feminist claims is characterized by the intensive use of social media to spread consciousness and amplify influence. For this research, we analyse three femitags (i.e., feminist hashtags) from Twitter that have been relevant in different crucial mobilizations in Argentina, Spain, and Mexico. These are three hashtags with different functions for activism that have shown special relevance due to their continuity or their intensity in the Spanish-speaking area between 2015 and 2020 (before the confinement due to the COVID-19 pandemic). #NiUnaMenos (#NotASingleWomanLess) started in Argentina in 2015 and called to massive mobilizations on the streets. #Cuéntalo (#TellIt) was initiated in Spain in 2018 for sexual abuse disclosure. #NiUnaMas (#NotASingleWomanMore) trended in México around 2020 to denounce every new victim of rape or femicide. We analyse how those hashtags have spread in the Spanish-speaking region, what kind of social actors have been involved and what has been the role of opinion leaders. All data were collected with academic access to the Twitter API during December 2021. We have found that the most influential actors in the conversation are contingent and circumstantial, the leadership structure tends towards horizontality, and opinion leaders with large numbers of followers are only important in very specific moments. In all cases, femitags serve as a toolbox for action and build up an archive of grievances with a transnational dimension. Furthermore, all of them point out that structural violence against women leads to feminicide.1.

Machine Learning Facemask Detection Models for COVID-19

With the breakout of the global pandemic known as COVID-19 it has forever changed ways of doing everyday things. Moreover, the discovery of new variants it has compelled regulatory authorities make the use of face mask in public places mandatory. Public places such as the public transport, shopping mall and universities where crowds of people come into contact with one another. It further exacerbates the issue by confining the masses in an indoor premise. As part of the enforcing the mandatory sop protocol work force or manpower is allocated that serve as gatekeepers to ensure the use of face mask. Due to the number of people at public places it increases the probability of human error. The solution is to incorporate the use of Artificial Intelligence that would use effective machine learning models to train and develop an effective and accurate facemask detection system. This study takes note of the existing system and develops one using the open-source library called TensorFlow to provide it with different variations of datasets that would simulate real world scenarios. With the implementation of the face mask detection system, it aims to replace manpower and allow artificial intelligence to conduct unsupervised operation that would be more efficient and effective. © 2022 IEEE.

X-Fidence: Post-Pandemic Wellness By Density Monitoring with Privacy Preservation

There is an increasing interest in crowd monitoring as we return to normal life after the Covid-19 pandemic. Some major obstacles to implementing a monitoring system are: privacy, scalability, and accuracy. Several entities have developed their own branded safety reassurance programs using the Internet of Things and sensors during the pandemic, and many cities are planning for the post-pandemic era. However, the lack of inclusiveness and the fear of personal information leakage often limit their usage. To this end, we developed X-Fidence, a real-time density monitoring system to avoid crowds which preserves user privacy. X-Fidence anonymously tracks visitors in certain places using Bluetooth technology. The system consists of a signal receiver module, a mobile application, and a monitoring server where the citywide current occupancy data syncs automatically. The mobile application provides density data in real-time and average historic crowds information that helps to make decisions more confidently. The X-Fidence mobile app warns residents if any area experiences more visitors than maximum safe capacity. We also present our results from an experiment to measure the feasibility and accuracy of the system. © 2022 IEEE.

3D Modeling for virtual tour of a Higher Education Institution

The pandemic caused by COVID 19 has generated crowds of people, this becomes a critical point that leads to a safe contagion. And it is for this reason that the idea of a virtual tour was born so that people can get to know the physical facilities of a Higher Education Institution (HEI) through a virtual environment. Universidad Tecnológica Israel (UISRAEL), does not have a digital space where its different areas can be appreciated by means of a 3D virtual tour. In this sense, this work presents the development of a 3D model, offering the user a digital interaction of the physical space from his home on his computer. The main benefit is that the user can get to know the facilities in an almost real way, generating a sense of orientation. This work has given way to a future virtual community at UISRAEL, making it possible to diversify and face the new challenges that virtualization brings with it. It will not only be a new means of information and communication, but also a space for interaction, entertainment and learning. All this leaves the basis for an application that in the future could develop more functionalities and be used in other research projects. © 2022 IEEE Computer Society. All rights reserved.

COMBINING CROWD AND MACHINE INTELLIGENCE TO DETECT FALSE NEWS ON SOCIAL MEDIA

The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. Machine intelligence has met with limited success in detecting and curbing false news. Human knowledge and intelligence hold great potential to complement machine-based methods. Yet they are largely underexplored in current false news detection research, especially in terms of how to efficiently utilize such information. We observe that the crowd contributes to the challenging task of assessing the veracity of news by posting responses or reporting. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a novel framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of crowd intelligence and the superior performance of the proposed framework in comparison with the benchmark methods. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation. This research significantly contributes to relevant literature on false news detection and crowd intelligence. In practice, our proposed framework serves as a feasible and effective approach for false news detection. [ FROM AUTHOR] Copyright of MIS Quarterly is the property of MIS Quarterly and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Monitoring social-distance in wide areas during pandemics: a density map and segmentation approach.

With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public spaces is of great importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by assessing social distancing in corridors up to small crowds by detecting each person individually, considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating social-distancing in wide areas, where important occlusions may be present. Our framework consists in the creation of new ground truth social distance labels, based on the ground truth density maps, and the proposal of two different solutions, a density-map-based and a segmentation-based, to detect crowds violating social-distancing constraints. We assess the results of both approaches by using the generated ground truth from the PET2009 and CityStreet datasets. We show that our framework performs well at providing the zones where people are not following the social-distance, even when heavily occluded or far away from the camera, compared to current detection and tracking approaches.

Congregating as a social phenomenon;the social glue that binds

Purpose>This study aims to consolidate and hone existing spectating and crowd theory. This is achieved by marrying socio-cultural ideas and concepts from related disciplines.Design/methodology/approach>This conceptual review examines what people do when they congregate at an event, and in doing so, answers the question of what they forgo when denied a crowd. Concepts are teased from the literature as to what happens during participatory congregation (in company, in situ), punctuated by relegation without it.Findings>Related concepts are organised into a typology. The metamodel is the essence of the paper and includes four themes: (1) identity construction, (2) interacting with others, (3) producing and co-producing the event and (4) the allure of tribalism.Research limitations/implications>The paper is conceptual and therefore a typology (not a taxonomy). This implies that while it is likely transferable, it is not generalisable. It is manual and subjective, as opposed to objective and automatic. Notwithstanding future research implications, it is intended to inform those considering running virtual events.Practical implications>Event organisers are informed as to the “what” and “why” of running community events. It encourages a more circumspect, humanistic view that events are not merely a source of revenue.Social implications>This review contributes a macro understanding of human nature, complementing a micro understanding of crowd behaviour.Originality/value>Virtual event management is a relatively new and burgeoning field. Prior to the Pandemic an event without a crowd was almost inconceivable.

PER-COVID (PEople pRoximity based on Certified and coOperative VIDeo-intelligence): A Software Tool for Physical Distancing and PPE Monitoring

The recent COVID-19 pandemic has led to a growing interest in IT tools for monitoring social distance and for checking the presence of personal protective equipment and whether it is worn properly. Correct monitoring in outdoor and indoor areas is essential to limit the spread of the virus and the risk of being infected. This paper presents PER-COVID, a software platform capable of monitoring crowds of people and the correct use of personal protective equipment in real time using innovative computer vision algorithms. The proposed system architecture and functional characteristics are illustrated, as well as some user interface screens are provided for simple interpretation and monitoring of critical events. © 2021 IEEE.

Model-based assessment of the risks of viral transmission in non-confined crowds.

This work assesses the risks of Covid-19 spread in diverse daily-life situations involving crowds of maskless pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchy observations, by coupling ad hoc spatial models for disease transmission via respiratory droplets to detailed field-data about pedestrian trajectories and head orientations. This allows us to rank the investigated situations by the infection risks that they present; importantly, the obtained hierarchy of risks is very largely conserved across transmission models: Street cafés present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets are comparatively quite low. While our models only approximate the actual transmission risks, their converging predictions lend credence to these findings. In situations with a moving crowd, density is the main factor influencing the estimated infection rate. Finally, our study explores the efficiency of street and venue redesigns in mitigating the viral spread: While the benefits of enforcing one-way foot traffic in (wide) walkways are unclear, changing the geometry of queues substantially affects disease transmission risks.

Heat-related illnesses in a mass gathering event and the necessity for newer diagnostic criteria: a field study.

Heat-related illnesses (HRIs), mainly heat exhaustion (HE) and heat stroke (HS), are characterized by an elevation of core body temperature. In this study, we aimed to explore the HRIs' types and patient characteristics among a sample taken from various representative in-field points in the Hajj season. A cross-sectional study was conducted in 2018 at 80 data collection points distributed in the field. Data related to demographics, features and risk factors were collected and analyzed from all encountered cases with suspected HRIs. Moreover, we developed a diagnostic tree for HRIs by using the XGBoost model. Out of the 1200 persons encountered during the study period, 231 fulfilled the criteria of HRIs spectrum and were included in this study. Around 6% had HS and 20% had HE. All HS cases (100%) were from outside of Saudi Arabia as compared with 72.5% diagnosed with HE (27.5% were from Saudi Arabia). In addition, 16% were considered as heat-induced muscle spasms, and 7% had limb heat edema. Additionally, most of HRIs cases were reported between 11 am and 1 pm. The HRIs diagnostic tree model gave a diagnostic accuracy of 93.6%. This study highlights the magnitude of HRIs among pilgrims in Hajj and provides a diagnostic tree that can aid in the risk stratification and diagnosis of these patients. We advise the implementation of more educational campaigns to pilgrims regarding preventable measures especially for the vulnerable groups (e.g. from outside Saudi Arabia, those with comorbidities and light-skinned people).

COVID-19 in context: Why do people die in emergencies? It's probably not because of collective psychology.

Notions of psychological frailty have been at the forefront of debates around the public response to the COVID-19 pandemic. In particular, there is the argument that collective selfishness, thoughtless behaviour, and over-reaction would make the effects of COVID-19 much worse. The same kinds of claims have been made in relation to other kinds of emergencies, such as fires, earthquakes, and sinking ships. We argue that in these cases as well as in the case of the COVID-19 pandemic, other factors are better explanations for fatalities - namely under-reaction to threat, systemic or structural factors, and mismanagement. Psychologizing disasters serves to distract from the real causes and thus from who might be held responsible. Far from being the problem, collective behaviour in emergencies - including the solidarity and cooperation so commonly witnessed among survivors - is the solution, one that should be harnessed more effectively in policy and practice.