A great deal of research has focused on the negative consequences of domestic violence (DV) on mental health. However, current studies cannot provide direct and reliable evidence on the impacts of DV on mental health in a short term as it is not feasible to measure mental health shortly before and after an unpredictable event like DV. This study aims to explore the short-term outcomes of DV on individuals' mental health. We collected a sample of 232 victims (77% female) and 232 nonvictims (gender and location matched with 232 victims) on Sina Weibo. In both the victim and nonvictim groups, we measured their mental health status during the 4 weeks before the first DV incident and during the 4 weeks after the DV incident. We used our proposed Online Ecological Recognition (OER) system, which is based on several predictive models to identify individuals' mental health statuses. Mental health statuses were measured based on individuals' Weibo profiles and messages, which included "Depression," "Suicide Probability," and "Satisfaction With Life." The results showed that mental health in the victim group was impacted by DV while individuals in the nonvictim group were not. Furthermore, the victim group demonstrated an increase in depression symptoms, higher suicide risks, and decreased life satisfaction after their DV experience. In addition, the effect of DV on individuals' mental health could appear in the conditions of child abuse, intimate partner violence, and exposure to DV. These findings inform that DV significantly impacts individuals' mental health over the short term, as in 4 weeks. Our proposed new data collection and analyses approach, OER, has implications for employing "big data" from social networks to identify individuals' mental health.