M
Marco Furini
Researcher at University of Modena and Reggio Emilia
Publications - 107
Citations - 3756
Marco Furini is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Computer science & The Internet. The author has an hindex of 20, co-authored 100 publications receiving 3493 citations. Previous affiliations of Marco Furini include University of Eastern Piedmont & University of Bologna.
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Proceedings ArticleDOI
5 Steps to Make Art Museums Tweet Influentially
TL;DR: An easily understandable framework is proposed to analyze the key content factors in museum conversations, including novel formulas for the evaluation of tweets and Twitter accounts influence, and 5 key steps that museums can perform in order to write more influential tweets.
Proceedings ArticleDOI
On the Usage of Smart Speakers During the Covid-19 Coronavirus Lockdown
TL;DR: 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.
Proceedings ArticleDOI
Untangling between fake-news and truth in social media to understand the Covid-19 Coronavirus
TL;DR: 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, and proposes an Awareness index to compute knowledge degree of volunteers.
Proceedings ArticleDOI
TagLecture: The gamification of video lecture indexing through quality-based tags
TL;DR: The gamification of the video indexing process is well accepted by students: more than 80% of participants liked the idea of using quality-based tags to classify video lectures, and more than half of them liked the Idea of a game where students challenge each other to tag pieces of video lectures.
Proceedings ArticleDOI
An audio/video analysis mechanism for web indexing
Marco Furini,Marco Aragone +1 more
TL;DR: This paper proposes a mechanism that integrates low and high level video features to provide a high level semantic description and an experimental evaluation shows the benefits of integrating audio and video analysis.