It seems a lifetime ago that we were sitting across from a date, nervously sipping our gin and tonics as they made awkward small talk. Allowing ourselves to think about the future has been an unreliable and rather futile activity since the coronavirus pandemic began. Nonetheless, OkCupid has thrown caution to the wind and made a prediction for when it believes will be the biggest dating day of 2021 in the UK. According to the dating app, Aug. 1 will be "the hottest day for dating in 2021," after Prime Minister Boris Johnson pledged to offer all UK adults the first dose of the COVID-19 vaccine by July 31. At the time of writing, 17.7 million people had received the first dose of the vaccine in the UK, and over 620,000 had received their second dose.
Berlin, Germany-based SaaS platform Adjust has released its dating app marketing guide. The guide has benchmarks, spotlights on industry leaders, and has tips on how app developers can retain users by the use of gender targeting, and in-app video streaming. Simple steps can make the difference between losing your online accounts or maintaining what is now a precious commodity: Your privacy. Over 270 million adults worldwide used dating apps in 2020 and almost two in five (39%) of US adults reported meeting their partner online. However, a major risk to an app's reputation is the presence of bots on the platform which frustrate the users or exposes them to scams.
As politicians play whack-a-mole with COVID-19 infection rates and try to balance the economic damage caused by lockdowns, stay-at-home orders have also impacted those out there in the dating scene. No longer able to meet up for a drink, a coffee, or now even a walk in the park, organizing an encounter with anyone other than your household or support bubble is banned and can result in a fine in the United Kingdom -- and this includes both dates and overnight stays. Therefore, the only feasible option available is online connections, by way of social networks or dating apps. Dating is hard enough at the best of times but sexual desire doesn't disappear just because you are cooped up at home. Realizing this, a number of healthcare organizations worldwide have urged us not to contribute to the spread of COVID-19 by meeting up with others for discreet sex outside of our social bubbles, bringing new meaning to the phrase, "You are your safest sex partner."
CLAIRE, the Confederation of Laboratories for AI Research in Europe, launched its COVID-19 Initiative in March 2020 as the first wave of the pandemic hit the continent. Its objective was to coordinate volunteer efforts from its members to contribute to tackling the effects of the disease. The taskforce was able to quickly gather a group of about 150 researchers, scientists and experts in AI organized into seven topic groups: epidemiological data analysis, mobility data analysis, bioinformatics, medical imaging, social dynamics monitoring, robotics, and scheduling and resource management. We brought you a comprehensive article about the activities of this initiative in one of last month's AI for Good series posts. You can read more about the outcomes and experience of this bottom-up approach in the article: The CLAIRE COVID-19 Initiative: a bottom-up effort from the European AI community.
This more than a yearlong outbreak is likely to have a significant impact on mental health of many individuals who lost loved ones, who lost personal contacts with others due to strictly enforced public health guidelines of mandatory social segregation. Complex psychological reactions to COVID-19 regulatory mechanisms of mandatory quarantine and related emotional reactions has been recognized as hard to disentangle  - . A study conducted in Belgium found social media being positively associated with constructive coping for adolescents with anxious feelings during the quarantine period of COVID-19 . Another study conducted among social media users during COVID-19 pandemic in Spain was able to capture added stress placed on people's emotional health during the pandemic period . However, social media providing a platform of risk communication and exchange of feelings and emotions to curb social isolation, this text data provides a wealth of information on the natural flow of people's emotional feelings and expressions . This rich source of data can be utilized to curb the data collection barriers during the pandemic. The goal of this research was to use AI to uncover the hidden, implicit signal related to emotional health of people subject to mandatory quarantine, embedded in a latent manner in their twitter messages. Within the context of this paper, an NLPbased emotion detection system aims to provide useful information by examining unstructured text data used in social media. The purpose of the NLP system used herein is to show the meaning and emotions of users' expressions related to a particular topic, which can be used to understand their psychological health and emotional wellbeing.
The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.
Couples who meet through smartphone dating apps are more motivated to move in together and have children, according to a new study. Researchers found that online daters have stronger long-term relationship goals than peers who hook up in more traditional ways - such as at the office or pub. Tinder and rivals such as Bumble, Match and Plenty of Fish have been criticised for fuelling casual sex. But, contrary to popular belief, spreading the net wider increases the chances of settling down with'Mr or Mrs Right', according to psychologists. An analysis of more than 3,000 over-18s in Switzerland showed couples who met on an app were more motivated by the idea of cohabiting.
CLAIRE, the Confederation of Laboratories for AI Research in Europe, launched its COVID-19 initiative in March 2020 as the first wave of the pandemic hit the continent. Its objective is to coordinate volunteer efforts of its members to contribute to tackling the effects of the disease. The taskforce was able to quickly gather a group of about 150 researchers, scientists and experts in AI organized in seven topic groups: epidemiological data analysis, mobility data analysis, bioinformatics, medical imaging, social dynamics monitoring, robotics, and scheduling and resource management. Activities of these groups yielded multiple outcomes including a publicly released resource on COVID-19 related data for drug-repurposing; the development the COVID-19 Infodemic Observatory to track spread of misinformation in social media and tools for the diagnosis based on CT scans using High Performance Computing (HPC) platforms. The latter was the catalyst for establishing a partnership between CLAIRE, the Italian National Inter-University Consortium for Informatics (CINI) and the Associazione Big Data (ABD) to provide HPC-enabled AI technologies to our network members.
Background: Misinformation spread through social media is a growing problem, and the emergence of COVID-19 has caused an explosion in new activity and renewed focus on the resulting threat to public health. Given this increased visibility, in-depth analysis of COVID-19 misinformation spread is critical to understanding the evolution of ideas with potential negative public health impact. Methods: Using a curated data set of COVID-19 tweets (N ~120 million tweets) spanning late January to early May 2020, we applied methods including regular expression filtering, supervised machine learning, sentiment analysis, geospatial analysis, and dynamic topic modeling to trace the spread of misinformation and to characterize novel features of COVID-19 conspiracy theories. Results: Random forest models for four major misinformation topics provided mixed results, with narrowly-defined conspiracy theories achieving F1 scores of 0.804 and 0.857, while more broad theories performed measurably worse, with scores of 0.654 and 0.347. Despite this, analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. We were able to identify distinct increases in negative sentiment, theory-specific trends in geospatial spread, and the evolution of conspiracy theory topics and subtopics over time. Conclusions: COVID-19 related conspiracy theories show that history frequently repeats itself, with the same conspiracy theories being recycled for new situations. We use a combination of supervised learning, unsupervised learning, and natural language processing techniques to look at the evolution of theories over the first four months of the COVID-19 outbreak, how these theories intertwine, and to hypothesize on more effective public health messaging to combat misinformation in online spaces.
The COVID-19 pandemic has caused international social tension and unrest. Besides the crisis itself, there are growing signs of rising conflict potential of societies around the world. Indicators of global mood changes are hard to detect and direct questionnaires suffer from social desirability biases. However, so-called implicit methods can reveal humans intrinsic desires from e.g. social media texts. We present psychologically validated social unrest predictors and replicate scalable and automated predictions, setting a new state of the art on a recent German shared task dataset. We employ this model to investigate a change of language towards social unrest during the COVID-19 pandemic by comparing established psychological predictors on samples of tweets from spring 2019 with spring 2020. The results show a significant increase of the conflict indicating psychometrics. With this work, we demonstrate the applicability of automated NLP-based approaches to quantitative psychological research.