Reading is a process that unfolds across space and time. Standard modeling approaches, however, overlook much of the spatio-temporal dynamics involved in reading by relying on aggregated reading measurements—typically only focusing on fixation durations—and employing models with strong simplifying assumptions. In this paper, we propose a generative model that captures not only how long fixations last, but also where they land and when they occur. To this end, we model reading scanpaths via two conditionally independent distributions: one for fixation location and timing, and another for fixation duration.The location (and timing) of fixation shifts, so-called saccades, are modeled using a spatio-temporal Hawkes process, which captures how each fixation excites the probability of a new fixation occurring near it in time and space. Empirically, our Hawkes process model exhibits higher likelihood on held-out reading data than baselines. The duration time of fixation events is modeled as a function of fixation-specific features convolved across time, thus capturing non-stationary delayed effects. We find that convolution-based approaches demonstrate weak predictive power when modeling disaggregated fixation durations. Similarly, our analysis of surprisal theory on disaggregated data reveals limited effectiveness in predicting both where fixations occur and how long they last.

Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently. But, the ability to both (1) extract events “in the wild” from text and (2) properly evaluate event detection systems has potential to support a wide variety of tasks such as monitoring the activity of socio-political movements, examining media coverage and public support of these movements, and informing policy decisions. Therefore, we study performance of the best event detection systems on detecting Black Lives Matter (BLM) events from tweets and news articles. The murder of George Floyd, an unarmed Black man, at the hands of police officers received global attention throughout the second half of 2020. Protests against police violence emerged worldwide and the BLM movement, which was once mostly regulated to the United States, was now seeing activity globally. This shared task asks participants to identify BLM related events from large unstructured data sources, using systems pretrained to extract socio-political events from text. We evaluate several metrics, accessing each system’s ability to identify protest events both temporally and spatially. Results show that identifying daily protest counts is an easier task than classifying spatial and temporal protest trends simultaneously, with maximum performance of 0.745 and 0.210 (Pearson r), respectively. Additionally, all baselines and participant systems suffered from low recall, with a maximum recall of 5.08.