## 3 Our Approach
### Overview of the High-Level Idea of Connecting SRA/IAT and Linguistic Statements  
Automatic measurement of attitude bias plays an essential role in many social computing studies, e.g., customized recommendation, public opinion analysis, influence analysis, and user profiling. Unlike the traditional psychological measurements in the laboratory, these tasks often require attitude measurement on a massive population instead of a limited sample set. Furthermore, different types of attitudes are expected for different applications. For example, in public opinion and influence analysis, people's explicit attitude bias is focused on, which is transferred in the group and influences each other through language. However, in customized recommendation and user profiling, people's implicit attitude bias can be more important, as it determines people's behavior inherently. In psychology studies, subjects' explicit bias of the concepts can be measured when the subjects are consciously aware of the measured concepts, e.g., being asked questions containing the concepts in SRA. Subjects' implicit bias of the concepts can be measured when the concepts are implicitly primed in subjects' minds, but they are not consciously aware of it, e.g., giving responses to the exemplars instead of concepts in IAT [1]. For this task, both classical psychological measurements and current computational sentiment or semantic measures have certain defects. First, in classical psychological measurements, although SRA and IAT can measure the explicit and implicit attitude separately with good theoretical validation, they have two main disadvantages for automatic massive measurements. 1. Sample size: Due to SRA and IAT requiring active cooperation from the subjects, the size and distribution of the sample size are highly limited by how many and what kind of subjects can be invited. 2. Independence: In both SRA and IAT, the subjects are aware that they are attending a measurement and may behave differently from their usual behaviors, i.e., the subjects cannot be exactly independent of the measurement. Second, in current language-based computational measurements, although sentiment and semantic analysis can be applied to massive subjects, the main disadvantage is ignoring the difference between explicit and implicit bias. This will be confused in the case that the explicit and implicit biases are different. For example, if a group of people expresses positive explicit bias toward Black people, but they have a negative implicit bias in their mind, the sentiment or semantic scores on their language about Black people can be neutral in general. This result can mislead both the public influence analysis (explicit bias) and the user preference profiling (implicit bias). To address the above problems, in this task, we propose to combine the advantages of psychological measurements and language-based computational measurements. Figure 3 illustrates the overview of the high-level idea of connecting SRA/IAT and linguistic statements. We simulate SRA and IAT with the sentiment of the statements containing combinations of concept/exemplar words (e.g., Flower/Rose) and attribute words (e.g., beautiful). In a set of statements expressing the attitude of the exemplars (e.g., Rose is beautiful. ), speakers'implicit bias can be understood statistically because there is a chance for every statement that the concepts (e.g., Flower) implicitly influence speakers' attitudes toward the exemplars (e.g., Rose), but the speakers are not consciously aware of it. Furthermore, to connect the criteria of SRA/IAT to the linguistic features, we make the following hypothesis. In the statements expressing the attitude of the concepts, speakers' explicit bias can be understood because the speakers must be consciously aware of the concepts they are discussing. For example, in Figure 3, the statement \"Flower is not beautiful at all\" must express the explicit bias on the concept \"Flower\".