Figure 1

Illustration of the procedure used to compute the dynamical user’s description vectors. The semantic network (panel a) is built by connecting two hashtags that appear together in the same tweet (panel b). The community structure of this network determines the topics that are discussed in the platform (the colors of the nodes code the communities). In order to obtain the dynamical user-topic matrices (panel d) we consider all the tweets in a sliding time window of seven days (panel c); each row corresponds to a single user and codes in the columns the number of times that the user has used one hashtag belonging to the corresponding topic during the considered period. The normalized sum over all the users of each column of the user-topic matrix (panel d) gives the average usage of each topic as a function of time, while the rows of the matrix give all the topics discussed by a single user. Finally, for each user one can obtain the vector \(\vec{d_{i}^{t}}\) (panel e) which gives the difference between the topics interesting user i at time t and the average usage of the topics over the population