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

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Social media platforms, such as Twitter, have been paramount in the COVID-19 context due to their ability to collect public concerns about the COVID-19 vaccination campaign, which has been underway to end the COVID-19 pandemic. This worldwide campaign has heavily relied on the actual willingness of individuals to get vaccinated independently of the language they speak or the country they reside. This study analyzes Twitter posts about Pfizer/BioNTech, Moderna, AstraZeneca/Vaxzevria, and Johnson & Johnson vaccines by considering the most spoken western languages. Tweets were sampled between April 15 and September 15, 2022, after the injections of at least three doses, collecting 9,513,063 posts that contained vaccine-related keywords. To determine the success of vaccination, temporal and sentiment analysis have been conducted, reporting opinion changes over time and their corresponding events whenever possible concerning each vaccine. Furthermore, we have extracted the main topics over languages providing potential bias due to the language-specific dictionary, such as Moderna in Spanish, and grouped them per country. Once performed the pre-processed procedure we worked with 8,343,490 tweets. Our findings show that Pfizer has been the most debated vaccine worldwide, and the main concerns have been the side effects on pregnant women and children and heart diseases.

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

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.