Opinion Leaders and Twitter: Metric Proposal and Psycholinguistic Analysis

Social media and personal health might be a dangerous combination: people are influenced by what they read online and don't pay attention to who wrote what they read. What happened during the COVID-19 pandemic? Who were the opinion leaders on social media? What were the conversations about? How did the health institutions communicate? To understand this, we focus on Twitter, and we analyze more than three million of Italian-written tweets posted from January 2020 to December 2021. We propose a method to identify opinion leaders and to analyze the content of the conversations. Results show that: (i) opinion leaders are linked to what they say and when they say it;(ii) politicians, newscast, and ordinary people accounts were able to become opinion leaders during the pandemic;(iii) conversations moved from a medical focus (at the beginning of the pandemic) to a social focus (in the last months of 2021);(iv) absence of health care institutions among opinion leaders. These results show that our approach might be useful for those who want to monitor the social scenario in terms of health (e.g., to identify as soon as possible accounts against or critical to medicine or to health authorities).

Twitter as Passive Sensor to Understand How COVID-19 Pandemic Affected Human Mobility

During the COVID-19 pandemic, countries all over the world have tried to prevent the spread of the virus with measures like social distancing, movement limitation, closure of premises and shops, voluntary isolation, lockdown, and curfew. Likely, these limitations have influenced the way people moved within urban spaces. In this study, we use Twitter as a passive sensor to understand how these measures affected human mobility. We focus on the city of Milan, one of the most international and active cities in Italy, but also one of the cities most affected by the spread of the virus. We analyzed more than one million of GPS geo-tagged tweets, posted from 2019 to 2022, and results show that the pandemic has affected human mobility (in 2022, less mobility during work hours and more mobility during the evening hours), and show that social and fashion-related activities are the main reasons people move within the city. This study shows the benefits of using Twitter as a passive sensor to measure human mobility: real-time analysis (not possible with interviews and/or questionnaire) and insights of the reasons behind human mobility (not possible to get with the sole use of telephone operators data). © 2023 IEEE.

On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

What happened on social media during the recent pandemic? Who was the opinion leader of the conversations? Who influenced whom? Were they medical doctors, ordinary people, scientific experts? Did health institutions play an important role in informing and updating citizens? Identifying opinion leaders within social platforms is of particular importance and, in this paper, we introduce the idea of a time sensitive interaction graph to identify opinion leaders within Twitter conversations. To evaluate our proposal, we focused on all the tweets posted on Twitter in the period 2020-21 and we considered just the ones that were Italian-written and were related to COVID-19. After mapping these tweets into the graph, we applied the PageRank algorithm to extract the opinion leaders of these conversations. Results show that our approach is effective in identifying opinion leaders and therefore it might be used to monitor the role that specific accounts (i.e., health authorities, politicians, city administrators) have within specific conversations. © 2022 ACM.

On using video lectures data usage to predict university students dropout

Technologies have changed many different aspects of people's life and the recent CoVid-19 pandemic proved that education is not an exception. But technologies in education go beyond the simple use of video lectures: technologies might be exploited to improve personal learning. In this paper, we focus on the dropout of studies, a global phenomenon that artificial intelligence techniques are trying to ameliorate. Here, we investigate whether data related to the consumption of video lectures might improve the students' dropout prediction. We consider first-year students enrolled in our Department and we characterize them with personal, scholastic, academic and technological features. Then, we measure the performance of three machine learning algorithms in terms of accuracy and sensitivity. The experimental evaluation shows that Random Forest and KNN perform better that Decision Tree and also shows that data related to the use of video lectures improves the prediction performance for some degree programs (reaching 73% in terms of accuracy and sensitivity). These preliminary results show that the approach is promising and worth exploring in future studies. © 2021 ACM.

Untangling between fake-news and truth in social media to understand the Covid-19 Coronavirus

"Covid-19 is a virus developed to rule the world" is just one of the many fake-news published on the Web. In this pandemic period, the Web is flooded with real news, allegedly true or blatantly false. To understand how fake news is affecting the Covid-19 perception, we selected 40 news (either true or fake) related to the origin, diffusion, treatment and effects of Covid-19 and we asked 293 volunteers to express their opinion on the truthfulness of the news. Then, we propose an Awareness index to compute knowledge degree of the volunteers. The results highlight a large ignorance on medical news, ignorance that goes beyond educational background. The study highlights the need for Health Institution to enter social media platforms in order to clearly explain what is true and what is false on Covid-19.

On the Usage of Smart Speakers during the Covid-19 Coronavirus Lockdown

The COVID-19 pandemic has dramatically changed every aspect of our professional and private life. It forced us to stay at home and it gave unprecedented power to technology. In this paper we focus on the emerging technology of smart speakers (e.g., Amazon Echo Plus, Google Home, etc.) and, through a developed questionnaire, we investigate how people use these devices. In particular, we analyze whether the human behavior has been affected by availability of these smart speaker. Results showed that the usage of these devices did not increase during lockdown, but it highlighted the presence of some privacy issues that might represent a burden to the diffusion of this type of technology. © 2020 ACM.

Can IoT Wearable Devices Feed Frugal Innovation?

Recently, there has been a lot of talk about Frugal services, that is, services that use existing technologies for a purpose other than the one for which they were designed. In this paper, we study whether the IoT wearable environment can be a fertile ground for the production of Frugal services. Through a real-world study, we investigate whether these devices are widespread, if there are obstacles that limit their diffusion, if the sensors they are equipped with are deemed reliable and, finally, if people who own them have an altruistic propensity or not. The results, from the frugal point of view, are encouraging: the IoT wearable environment seems to be pervasive enough and ubiquitous, without great obstacles for their adoption. The provided sensors seem to be generally reliable, whereas the altruistic propensity might be questioned: in general, people are not inclined to share, but if the goal is clear (in our case we hypothesized a fight against Covid-19), altruistic propensity grows a lot. © 2020 ACM.