# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 3 Materials and Methods  
### Data  
For the assessment of mood states, the POMS has been widely used. It was compiled by [PERSON] et al. in 1971 and was initially used to measure the psychological health of individuals. The POMS consists of 65 items and six mood scales (Tension-Anxiety, Depression-Dejection, Anger-Hostility, Vigor-Activity, Fatigue-Inertia, and Confusion-Bewilderment). [PERSON] et al. later developed an abbreviated POMS, which contains 40 items and seven mood scales, including an esteem-related affect, and verified its reliability and validity [12]. Participants rated each item on a five-point answer scale. The score of each mood dimension equals the sum of scores of several particular questions. The total score of abbreviated POMS, called total mood disturbance, is calculated by subtracting the scores of two positive dimensions from the five negative dimensions and adding 100. Therefore, the higher the total score is, the more negative mood one may have.  
With the permission of participants, researchers gained access to their Weibo home address and then obtained detailed information and blog messages using a Web crawler. The experiment  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Emotion Expression** & **Mood State** & **Sentiment** \\ \hline Duration & Relatively long & Relatively short \\ Time mode & Gradual, continuous & Rapid, episodic \\ Strength & Relatively weak & Relatively strong \\ Cause & Caused by specific events & Caused by specific events \\ Function & Shows current state information of users & Shows current state information of context \\ Orientation & No orientation & Points to specific event \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table of mood and sentiment [24; 26].  
was conducted between June 1 and 3, 2016 and comprised 224 users, including 110 males and 114 females. The experiment was conducted in a campus and the participants were college students. The subjects ranged in age from 17 to 28 and there were 27 individuals younger than 20, 101 individuals between 20 and 22, 92 individuals in the range of 23 to 25 years of age, and 4 individuals older than 25. The participants consisted of 72 individuals with a bachelor's degree, 126 individuals with a masters, five PhD degree, and 21 individuals with education below an undergraduate level. The inactive user was defined as someone who had no more than 50 friends and no blog updates within three months prior to the experiment. The inactive users were considered invalid and excluded and 132 active users were selected. The questionnaire result showed that these individuals were under normal psychological state considering that the extreme value of POMS score did not reach the bounds.  
The design of this study is shown in Figure 1. The mood states of social network users were obtained through the Profile of Mood State. The structured data such as registration date and number of followers could be captured from the Weibo home page directly and comprised the structural features. The text information of microblogging was processed via a lexicon-based method to generate the sentiment related features. After the feature extraction, a multitask regression model was adopted and trained.