Towards Tweet Content Suggestions for Museum Media Managers

Furini, Marco; Mandreoli, Federica; Montangero, Manuela

doi:10.1145/3284869.3284874

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…[8] aims to understand more about how museums exploit social networks by studying the types of tweets and the kinds of activities museums perform to engage their visitors, and [9] proposes a five-step strategy to make art museums influential over Twitter. The interested reader can refer to [10][11][12][13] for further relevant research.…”

Section: Scenario 1: Social Network Analyticsmentioning

confidence: 99%

About Challenges in Data Analytics and Machine Learning for Social Good

Montangero

2022

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The large number of new services and applications and, in general, all our everyday activities resolve in data mass production: all these data can become a golden source of information that might be used to improve our lives, wellness and working days. (Interpretable) Machine Learning approaches, the use of which is increasingly ubiquitous in various settings, are definitely one of the most effective tools for retrieving and obtaining essential information from data. However, many challenges arise in order to effectively exploit them. In this paper, we analyze key scenarios in which large amounts of data and machine learning techniques can be used for social good: social network analytics for enhancing cultural heritage dissemination; game analytics to foster Computational Thinking in education; medical analytics to improve the quality of life of the elderly and reduce health care expenses; exploration of work datafication potential in improving the management of human resources (HRM). For the first two of the previously mentioned scenarios, we present new results related to previously published research, framing these results in a more general discussion over challenges arising when adopting machine learning techniques for social good.

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Section: Scenario 1: Social Network Analyticsmentioning

confidence: 99%

About Challenges in Data Analytics and Machine Learning for Social Good

Montangero

2022

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“…Tweets have been the basis of many different analyses (from stock market prediction [4] to TV promotions [5], from geographical discovery [6] to cultural heritage promotion [15]). In this paper, building on past expertise in graph and social analytics in different contexts (e.g., cultural heritage [13][14][15]21] and HRM [3,9]) we use Twitter conversations to address the following research questions: RQ Who are the opinion leaders within these conversations?…”

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confidence: 99%

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

Furini

Mariotti

Montangero

2022

Proceedings of the 2022 ACM Conference on Information Technology for Social Good

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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.

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Invited Speech: Data Analytics and (Interpretable) Machine Learning for Social Good

Martoglia

2021

2021 IEEE 23rd Int Conf on High Performance Computing &Amp; Communications; 7th Int Conf on Data Science &Amp; Systems; 19th In

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Towards Tweet Content Suggestions for Museum Media Managers

Cited by 7 publications

References 23 publications

About Challenges in Data Analytics and Machine Learning for Social Good

About Challenges in Data Analytics and Machine Learning for Social Good

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

Invited Speech: Data Analytics and (Interpretable) Machine Learning for Social Good

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