Online Social Networks (OSN) are privileged observation channels for understanding the geospatial facets of many real-world phenomena [1]. Unfortunately, in most cases OSN content lacks explicit and structured geographic information, as in the case of Twitter, where only a minimal fraction (1% to 4%) of messages are natively geotagged [2]. This shortage of explicit geographic information drastically limits the exploitation of OSN data in geospatial Decision Support Systems (DSS) [3]. Conversely, the prompt availability of geotagged content would empower existing systems and would open up the possibility to develop new and better geospatial services and applications [4, 5]. As a practical example of this kind, several social media-based systems have been proposed in recent years for mapping and visualizing situational information in the aftermath of mass disasters - a task dubbed as crisis mapping - in an effort to augment emergency response [6, 7]. These systems, however, demand geotagged data to be placed on crisis maps, which in turn imposes to perform the geoparsing task on the majority of social media content. Explicit geographic information is not only needed in early warning [8, 9] and emergency response systems [10, 11, 12, 13, 14], but also in systems and applications for improving event promotion [15, 16], touristic planning [17, 18, 19], healthcare accessibility [20], news aggregation [21] Post-print of the article published in Decision Support Systems 136, 2020. Please refer to the published version: doi.org/10.1016/j.dss.2020.113346

Over the past year firms like AggregateIQ and Cambridge Analytica have been credited with using AI-targeted ads on social media to help swing the Brexit referendum and the US presidential election respectively. But a lack of evidence meant we have never known whether the technology exists to make this possible. Now the first study detailing the process from start to finish is finally shedding some light. "This is the first time that I've seen all the dots connected," says Joanna Bryson, an artificial intelligence researcher at the University of Bath, UK. At the heart of the debate is psychographic targeting – the directing of political campaigns at people via social media based on their personality and political interests, with the aid of vast amount of data filtered by artificial intelligence.

Over the past year firms like AggregateIQ and Cambridge Analytica have been credited with using AI-targeted ads on social media to help swing the Brexit referendum and the US presidential election respectively. But a lack of evidence meant we have never known whether the technology exists to make this possible. Now the first study detailing the process from start to finish is finally shedding some light. "This is the first time that I've seen all the dots connected," says Joanna Bryson, an artificial intelligence researcher at the University of Bath, UK. At the heart of the debate is psychographic targeting – the directing of political campaigns at people via social media based on their personality and political interests, with the aid of vast amount of data filtered by artificial intelligence.