Robust effect of justice for others: Evidence from word embedding association test

Abstract

Maintenance of control and non-randomness is the fundamental need of human beings, and people are motivated to see social and physical environments as stable and orderly, so that the belief about the controlled and nonrandom world is protected. According to just motive theory, people tend to believe in a just world, in which good deeds get rewarded and bad evils get punished, and this belief helps individuals get the sense of control. Otherwise, it is difficult for individuals to pursuit long-term goals and obey social rules in ordinary lives. Recent evidence shows that there are self-others’ distinctions regarding belief in a just world (BJW). In particular, BJW for others is found to be processed more primitively and influenced more possibly by cultural norms, whereas BJW for the self is processed more elaborately and influenced more possibly by personal experiences. And BJW for others is more related to the indicators of human values and social attitudes, such as world assumption and justice restoration, whereas BJW for the self is more related to the indicators of personal wellness, such as self-esteem and mental health. More important, justice motive is an implicit process, and in particular justice motive for others (vs. for the self) plays a critical role in shaping human values and social attitudes, which is often hidden in the explicit measurement because of social desirability and sample bias. However, it is difficult for researchers to measure the implicit process of justice motive, especially for others’ lives and the whole society. Many studies, especially those conducted among college students and middle class populations, yielded that BJW for others was endorsed lower than BJW for the self. Therefore, more and more researchers recommend that BJW(s) should be measured through implicit tests and large-scale samples. Based on machine learning and Word Embedding Association Test (WEAT), the present research was to test the robust effect of justice for others in terms of the word or sentence vector similarity, with a greater similarity indicating a higher level of semantic association. First, in the word vector model of the Sina Weibo corpus, the word vector similarity of “others” and “justice” was significantly greater than that of “self” and “justice”, and this pattern was replicated through the word vector model of a comprehensive Weibo corpus. Further, we calculated the sentence vector through the Google BERT model, and found that the sentence vector similarity of “others” and “justice” was also significantly greater than that of “self” and “justice”. In conclusion, both results via word vector and sentence vector demonstrate the robust effect of others regarding implicit just-world beliefs, in that as compared with one’s own experiences, people are more likely to believe that others’ world or the whole society is a just place. Furthermore, this study also suggests that internet users’ fundamental need for social order is automatically embedded in the cyberspace which is seemingly random and disordered, and the WEAT provides a new method to measure human’s social attitude behind the unstructured texts.