A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets

Andreadis, Stelios; Antzoulatos, Gerasimos; Mavropoulos, Thanassis; Giannakeris, Panagiotis; Tzionis, Grigoris; Pantelidis, Nick; Ioannidis, Konstantinos; Karakostas, Anastasios; Gialampoukidis, Ilias; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.1016/j.osnem.2021.100134

Cited by 41 publications

(20 citation statements)

References 60 publications

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“…Other works concerned the spatial and temporal distribution of tweets related to the pandemic through the use of interactive visual analytics technologies, such as interactive maps and dashboards [18]. The aim of these visualization tools is to enable policy makers and public authorities to view the increasing social media activity as the contagion spreads [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

et al. 2021

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The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.

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Section: Literature Reviewmentioning

confidence: 99%

Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

et al. 2021

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“…To this end, we perform an automatic geotagging methodology, presented and evaluated in [16], that transforms English tweets into georeferenced data by using their textual content to detect mentioned locations. After proper preprocessing, we employ NER techniques in the form of a pre-trained biLSTM-based model [17] to retrieve location-type mentions in the tweet's text.…”

Section: A Collection and Geotagging Of Social Media Datamentioning

confidence: 99%

Earthquakes: From Twitter Detection to EO Data Processing

Andreadis

Gialampoukidis

Manconi³

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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The increase of social media use in recent years has shown potential also for the identification of specific trends in the data that could be used to locate earthquakes. In this work, we implemented a pipeline that uses Twitter data to identify locations of earthquakes and use the information to trigger EO data analysis. We tested the pipeline for almost a year over Japan, an area where earthquake events are frequent, as well as the use of social media in the population. Here, we show the results and discuss the potential development of such procedures. In the future, considering the rapid development and the increase of satellite constellations aimed at global coverage with short revisit times, algorithms of this kind could be used to prioritize satellite acquisitions for the detection of the areas most affected by earthquake damages.

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“…Additionally, modularity scores higher than 0 means that there is a possible presence of a community. Accuracy Precision Recall Modularity Fscore [36] A researcher developed two-stage framework based on [37] and LDA Zachary's karate club [38], dolphin social network [39], political blog dataset [40] 0,600 N/A N/A 0,915 N/A [41] Louvain [42] A user-made dataset containing an unknown number of tweets N/A N/A N/A 0,870 N/A [43] Louvain [42] and its implementation [44] A Spectral Clustering [52] & Node2Vec [53] [38], [39], [54], [55], [56], [57], [58] N/A N/A N/A 0,596 N/A [59] Louvain [42] A user-made dataset containing 6.7 million tweets 0,755…”

Section: Community Detectionmentioning

confidence: 99%

“…Extended version of Louvain [42] [61] N/A 0,280 N/A N/A 0,342 [62] Louvain [42], LPA [63], walktrap [64], Infomap [65] HCR [66], OMD […”

Section: Community Detectionmentioning

confidence: 99%

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Systematic Literature Review of Detecting Topics and Communities in Social Networks

ŞENCAN¹,

Atacak²,

Doğru³

2022

Bilişim Teknolojileri Dergisi

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In the recent past and in today’s world, the internet is advancing rapidly and is easily accessible; this growth has made the social media platforms such as Facebook, Instagram, Twitter, and LinkedIn widely used which produces big data. This requires both topic Detection applications in order to access the required information, as well as community detection practices in order to provide collective services to communities that can be referred to as individuals with similar interests and opinions over the same subject. Therefore, it is vital for researchers to conduct research on topic detection and community detection research areas in social networks and to develop methods and techniques for problem-solving. In this study, a systematic and in-depth literature review is provided on studies that conduct topic and community analysis on social media platforms to provide a comprehensive overview of the given areas. Most of the studies to be analyzed are selected from articles using machine learning-based models that are known to achieve successful results in practice. As a result of the analysis of these studies; it has been concluded that a single model cannot be proposed in the area of topic detection and that the appropriate model should only be selected or created in a problem-specific way, taking into account all the characteristics of the given problem, while the Louvain method seems to stand out with its results in terms of performance in the area of community detection.

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A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets

Cited by 41 publications

References 60 publications

Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

Earthquakes: From Twitter Detection to EO Data Processing

Systematic Literature Review of Detecting Topics and Communities in Social Networks

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