Governments and agencies are struggling to coordinate effective disaster relief programs. Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) can help. Natural disasters wreak havoc around the world every year. But it's hard to appreciate the scale of this damage. And that doesn't include the Northern California Wildfires or the biggest hurricanes.

Geo-tagged tweets can potentially help with sensing the interaction of people with their surrounding environment. Based on this hypothesis, this paper makes use of geotagged tweets in order to ascertain various land uses with a broader goal to help with urban/city planning. The proposed method utilises supervised learning to reveal spatial land use within cities with the help of Twitter activity signatures. Specifically, the technique involves using tweets from three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical results are checked against the zoning data provided by respective city councils and a good match is observed between the predicted land use and existing land zoning by the city councils. We show that geo-tagged tweets contain features that can be useful for land use identification.

Disinformation has become a central feature of the COVID-19 crisis. According to a recent poll, false or misleading information about the pandemic reaches close to half of all online news consumers in the U.K. As this type of malign information and high-tech "deepfake" imagery can spread so fast online, it poses a risk to democratic societies worldwide by increasing public mistrust in governments and public authorities -- a phenomenon referred to as "truth decay." New research, however, highlights new ways to detect and dispel disinformation online. There are several factors that may account for the rapid spread of disinformation during the COVID-19 pandemic. Given the global nature of the pandemic, more groups are using disinformation to further their agendas.

Artificial intelligence is being used to spot the difference between human users and fake accounts on Twitter. Researchers at the University of Southern California (USC) have trained an artificial intelligence system to detect Twitter bots based on differences in the patterns of Tweeting activity of real and fake accounts. The researchers analyzed two separate datasets of Twitter users, which were classified manually or by a pre-existing algorithm as either bot or human. The manually verified data set included 8.4 million tweets from 3,500 human accounts, and 3.4 million tweets from 5,000 bots. The team found that human users replied between four and five times more frequently to other tweets than bots did.

Humans' unique laziness when it comes to interacting on social media could be the key to telling us apart from artificially intelligent'bots', a new study shows. US researchers have identified behavioural trends of humans on Twitter that are absent in social media bots – namely a decrease in tweet length over time. The team studied how the behaviour of humans and bots changed over the course of a session on Twitter relating to political events. While humans get lazier as sessions progress and can't be bothered typing out long tweets, bots maintain consistent levels of engagement over time. Such a behavioural difference could inform new machine learning algorithms for bot detection software.

Artificial intelligence is being used to spot the difference between human users and fake accounts on Twitter. Emilio Ferrara at the University of Southern California in the US, and his colleagues have trained an AI to detect bots on Twitter based on differences in patterns of activity between real and fake accounts. The team analysed two separate datasets of Twitter users, which had been classified either manually or by a pre-existing algorithm as either bot or human. The manually verified dataset consisted of 8.4 million tweets from 3500 human accounts, and 3.4 million tweets from 5000 bots. The researchers found that human users replied four to five times more often to other tweets than bots did.