COVIDSensing: Social Sensing Strategy for the Management of the COVID-19 Crisis

The management of the COVID-19 pandemic has been shown to be critical for reducing its dramatic effects. Social sensing can analyse user-contributed data posted daily in social-media services, where participants are seen as Social Sensors. Individually, social sensors may provide noisy information. However, collectively, such opinion holders constitute a large critical mass dispersed everywhere and with an immediate capacity for information transfer. The main goal of this article is to present a novel methodological tool based on social sensing, called COVIDSensing. In particular, this application serves to provide actionable information in real time for the management of the socio-economic and health crisis caused by COVID-19. This tool dynamically identifies socio-economic problems of general interest through the analysis of people’s opinions on social networks. Moreover, it tracks and predicts the evolution of the COVID-19 pandemic based on epidemiological figures together with the social perceptions towards the disease. This article presents the case study of Spain to illustrate the tool.

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

Social Sensors for Wildlife: Ecological Opportunities in the Era of Camera Ubiquity

Expansive study areas, such as those used by highly-mobile species, provide numerous logistical challenges for researchers. Community science initiatives have been proposed as a means of overcoming some of these challenges but often suffer from low uptake or limited long-term participation rates. Nevertheless, there are many places where the public has a much higher visitation rate than do field researchers. Here we demonstrate a passive means of collecting community science data by sourcing ecological image data from the digital public, who act as “eco-social sensors,” via a public photo-sharing platform—Flickr. To achieve this, we use freely-available Python packages and simple applications of convolutional neural networks. Using the Weddell seal (Leptonychotes weddellii) on the Antarctic Peninsula as an example, we use these data with field survey data to demonstrate the viability of photo-identification for this species, supplement traditional field studies to better understand patterns of habitat use, describe spatial and sex-specific signals in molt phenology, and examine behavioral differences between the Antarctic Peninsula’s Weddell seal population and better-studied populations in the species’ more southerly fast-ice habitat. While our analyses are unavoidably limited by the relatively small volume of imagery currently available, this pilot study demonstrates the utility an eco-social sensors approach, the value of ad hoc wildlife photography, the role of geographic metadata for the incorporation of such imagery into ecological analyses, the remaining challenges of computer vision for ecological applications, and the viability of pelage patterns for use in individual recognition for this species.

A call for governments to pause Twitter censorship: a cross-sectional study using Twitter data as social-spatial sensors of COVID-19/SARS-CoV-2 research diffusion

ABSTRACTObjectivesTo determine whether Twitter data can be used as social-spatial sensors to show how research on COVID-19/SARS-CoV-2 diffuses through the population to reach the people that are especially affected by the disease.DesignCross-sectional bibliometric analysis conducted between 23rd March and 14th April 2020.SettingThree sources of data were used in the analysis: (1) deaths per number of population for COVID-19/SARS-CoV-2 retrieved from Coronavirus Resource Center at John Hopkins University and Worldometer, (2) publications related to COVID-19/SARS-CoV-2 retrieved from WHO COVID-19 database of global publications, and (3) tweets of these publications retrieved from Altmetric.com and Twitter.Main Outcome(s) and Measure(s)To map Twitter activity against number of publications and deaths per number of population worldwide and in the USA states. To determine the relationship between number of tweets as dependent variable and deaths per number of population and number of publications as independent variables.ResultsDeaths per one hundred thousand population for countries ranged from 0 to 104, and deaths per one million population for USA states ranged from 2 to 513. Total number of publications used in the analysis was 1761, and total number of tweets used in the analysis was 751,068. Mapping of worldwide data illustrated that high Twitter activity was related to high numbers of COVID-19/SARS-CoV-2 deaths, with tweets inversely weighted with number of publications. Poisson regression models of worldwide data showed a positive correlation between the national deaths per number of population and tweets when holding the country’s number of publications constant (coefficient 0.0285, S.E. 0.0003, p<0.001). Conversely, this relationship was negatively correlated in USA states (coefficient –0.0013, S.E. 0.0001, p<0.001).ConclusionsThis study shows that Twitter can play a crucial role in the rapid research response during the COVID-19/SARS-CoV-2 global pandemic, especially to spread research with prompt public scrutiny. Governments are urged to pause censorship of social media platforms during these unprecedented times to support the scientific community’s fight against COVID-19/SARS-CoV-2.SUMMARY BOXWhat is already known on this topicTwitter is progressively being used by researchers to share information and knowledge transfer.Tweets can be used as ‘social sensors’, which is the concept of transforming a physical sensor in the real world through social media analysis.Previous studies have shown that social sensors can provide insight into major social and physical events.What this study addsUsing Twitter data used as social-spatial sensors, we demonstrated that Twitter activity was significantly positively correlated to the numbers of COVID-19/SARS-CoV-2 deaths, when holding the country’s number of publications constant.Twitter can play a crucial role in the rapid research response during the COVID-19/SARS-CoV-2 global pandemic.

Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population (Preprint)

BACKGROUND Infoveillance is an application from Infodemiology field with the aim to monitor public health and create public policies. Social sensor is the people providing thought, ideas through electronic communication channels(i.e. Internet). The actual scenario is related to tackle the covid19 impact over the world, many countries have the infrastructure, scientists to help the growth and countries took actions to decrease the impact. South American countries have a different context about Economy, Health and Research, so Infoveillance can be a useful tool to monitor and improve the decisions and be more strategical. The motivation of this work is analyze the capital of Spanish Speakers Countries in South America using a Text Mining Approach with Twitter as data source. The preliminary results helps to understand what happens two weeks ago and opens the analysis from different perspectives i.e. Economics, Social. OBJECTIVE Analyze the behaviour of South American Capitals in front of covid19 pandemics and show the helpfulness of Text Mining Approach for Infoveillance tasks. METHODS Text Mining process RESULTS - Argentina and Venezuela capitals are the biggest number of post during this period, opposite with Bolivia, Ecuador and Uruguay. - Most relevant users are related to mass media like radio, television or newspapers. - There is a general concern about covid19 but every country talks about different areas: Economics, Health, Environmental Impact. CONCLUSIONS Infoveillance based on Social Sensors with data coming from Twitter can help to understand the trends on the population of the capitals. Besides, it is necessary to filter the posts for processing the text and get insights about frequency, top users, most important terms. This data is useful to analyse the population from different approaches. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1101/2020.04.06.20055749

Infoveillance based on Social Sensors to Analyze the impact of Covid19 in South American Population

ABSTRACTInfoveillance is an application from Infodemiology field with the aim to monitor public health and create public policies. Social sensor is the people providing thought, ideas through electronic communication channels(i.e. Internet). The actual scenario is related to tackle the covid19 impact over the world, many countries have the infrastructure, scientists to help the growth and countries took actions to decrease the impact. South American countries have a different context about Economy, Health and Research, so Infoveillance can be a useful tool to monitor and improve the decisions and be more strategical. The motivation of this work is analyze the capital of Spanish Speakers Countries in South America using a Text Mining Approach with Twitter as data source. The preliminary results helps to understand what happens two weeks ago and opens the analysis from different perspectives i.e. Economics, Social.