A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.Comment: Accepted by EMNLP 201

Supplemental Material for “More than meets the tie: Examining the Role of Interpersonal Relationships in Social Networks”

This is the supplementary material for the paper “More than meets the tie: Examining the Role of Interpersonal Relationships in Social Networks” accepted by the International Conference of Web and Social Media (ICWSM'21).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167015/3/Supp_ICWSM21.pdfDescription of Supp_ICWSM21.pdf : Supplementary materialSEL

Estimating mobility of tourists. New Twitter-based procedure

Twitter has been actively researched as a human mobility proxy. Tweets can contain two classes of geographical metadata: the location from which a tweet was published, and the place where the tweet is estimated to have been published. Nevertheless, Twitter also presents tweets without any geographical metadata when querying for tweets on a specific location. This study presents a methodology which includes an algorithm for estimating the geographical coordinates to tweets for which Twitter doesn't assign any. Our objective is to determine the origin and the route that a tourist followed, even if Twitter doesn't return geographically identified data. This is carried out through geographical searches of tweets inside a defined area. Once a tweet is found inside an area, but its metadata contains no explicit geographical coordinates, its coordinates are estimated by iteratively performing geographical searches, with a decreasing geographical searching radius. This algorithm was tested in two touristic villages of Madrid (Spain) and a major city in Canada. A set of tweets without geographical coordinates in these areas were found and processed. The coordinates of a subset of them were successfully estimated.Agencia Estatal de Investigación | Ref. PID2020-116040RB-I00Universidade de Vigo/CISU

Conspiracy theories under the lens of social psychology

openIl cospirazionismo inteso come aderire a una teoria della cospirazione al fine di spiegare gli eventi sociali è un fenomeno che non riguarda solo la nostra epoca, infatti esiste già dall’antichità, ma è diventato particolarmente rilevante in questo momento storico, dove eventi di crisi come la pandemia di Covid-19 mettono in dubbio le certezze fornite dal metodo scientifico e dalle istituzioni. Vari studi negli anni si sono occupati di spiegare le cause individuali e sociali della formazione e della credibilità delle teorie del complotto riguardo i temi più disparati ma si occupano anche di riportare nel complesso le conseguenze sociali e politiche delle teorie della cospirazione, e delle possibili soluzioni al fine di comprendere come gestire il fenomeno