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We have retrieved and analyzed several millions of Twitter messages corresponding to the Spanish general elections held on the 20th of December 2015 and repeated on the 26th of June 2016. The availability of data from two electoral campaigns that are very close in time allows us to compare collective behaviors of two analogous social systems with a similar context. By computing and analyzing the time series of daily activity, we have found a significant linear correlation between both elections. Additionally, we have revealed that the daily number of tweets, retweets, and mentions follow a power law with respect to the number of unique users that take part in the conversation. Furthermore, we have verified that the topologies of the networks of mentions and retweets do not change from one election to the other, indicating that their underlying dynamics are robust in the face of a change in social context. Hence, in the light of our results, there are several recurrent collective behavioral patterns that exhibit similar and consistent properties in different electoral campaigns.

Mounting evidence suggests that science and engineering fields suffer from gender biases. In this paper, we study the physics community, a discipline where women are still under-represented and gender disparities persist. To reveal such inequalities, we perform a paper matching analysis using a robust statistical similarity metric. Our analyses indicate that women’s papers tend to have lower visibility in the global citation network, a phenomenon significantly influenced by the temporal aspects of scientific production. Within pairs of similar papers, the authors that publish first tend to obtain more citations. From the group perspective, men have cumulative historical advantages due to women joining the field later and at a slower rate. Altogether, these results indicate that the first-mover advantage plays a crucial role in the emergence of gender disparities in citations of women-authored papers in the physics community.

The communication and migration patterns of a country are shaped by its socioeconomic processes. The economy of Senegal is predominantly rural, as agriculture employs over 70% of the labor force. In this paper, we use mobile phone records to explore the impact of agricultural activity on the communication and mobility patterns of the inhabitants of Senegal. We find two peaks of phone calls activity emerging during the growing season. Moreover, during the harvest period, we detect an increase in the migration flows throughout the country. However, religious holidays also shape the mobility patterns of the Senegalese people. Hence, in the light of our results, agricultural activity and religious holidays are the primary drivers of mobility inside the country.