## 4 Study 2. Knowledge-Driven and Data-Driven Competence
### Discussion  
In study 2, we explored individual competence from livestream videos at a smaller granularity from a data-driven perspective. With the crawled livestream videos from the livestream platform, we first constructed the CH-CMD and sent the livestream videos to a crowdsourcing platform for competence annotation. Then, we extracted features based on three modalities: visual, vocal, and textual. Finally, we utilized an efficient deep learning algorithm, LMF, to build our competence prediction model with the input data from the three modalities. Our research confirmed the necessity of multimodal analysis in the livestream context, with the trimodal model (accuracy reaching 87.20%) outperforming the unimodal (accuracy by over 7%) and bimodal models (accuracy by approximately 3%). Most prior studies examined individual competence with intramodality information, but in the real world, modalities interact with each other, and they may influence individuals' perceptions. For instance, visual information can influence the impact of vocal behaviors [100], and an individual's verbal statements may affect his visual perception [101]. Therefore, it is essential to make predictions with full cross-modal information and employ multimodal analysis in our research. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multirow{3}{*}{\
\begin{tabular}{c} **Deep** \\ **Learning** \\ **Model** \\ \end{tabular} } & \multicolumn{3}{c}{**Data Modalities Used by the Model**} & \multirow{3}{*}{**Accuracy**} & \multirow{3}{*}{**(a)**} & \multirow{3}{*}{**(b)**} & \multirow{3}{*}{**(c)**} & \multirow{3}{*}{**(d)**} \\  & & & & & & & **F1-Score** & & \\ \cline{5-8}  & & & **Textual** & & & & & **F1-Score** & & \\ \hline (1) & \multirow{3}{*}{
\begin{tabular}{c} No fusion + \\ unimodal data \\ \end{tabular} } & \multirow{3}{*}{LMF} & \(\surd\) & & & 0.8055 & 0.8057 & 0.7353 & 0.644 \\ \cline{5-8}  & & & & \(\surd\) & & 0.8207 & 0.8211 & 0.7015 & 0.6951 \\ \cline{5-8}  & & & & \(\surd\) & 0.8328 & 0.832 & 0.6701 & 0.6893 \\ \cline{5-8}  & & & \(\surd\) & \(\surd\) & & 0.8419 & 0.8418 & 0.724 & 0.6471 \\ \cline{5-8}  & & & \(\surd\) & & \(\surd\) & 0.845 & 0.8453 & 0.6581 & 0.6983 \\ \cline{5-8}  & & & \(\surd\) & \(\surd\) & 0.8419 & 0.7293 & 0.724 & 0.6471 \\ \cline{5-8}  & & EF\_LSTM & \(\surd\) & \(\surd\) & \(\surd\) & 0.8297 & 0.8299 & 0.7415 & 0.6304 \\ \cline{5-8}  & & & LMF & \(\surd\) & \(\surd\) & 0.8723 & 0.8717 & 0.6347 & 0.7188 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Modality ablation results. In addition, the results of study 2 provided valuable insights into the impact of individual features in the visual, vocal, and textual modalities on perceived competence. First, the results of unimodal models indicate that models with visual features perform better than models with vocal or textual features (see lines 1 to 3 in Table 6). Second, the bimodal model with the visual modality performed better (see lines 4 to 6 in Table 6). These results indicate that the visual modality contributes the most to perceived individual competence, followed by the vocal modality, and then the textual modality. It seems that \"how an individual looks\" and \"how he speaks\" are more important than \"what he says\". This is because the transcripts used by influencers in livestreams generally have similar patterns and wording [102], so competence is mainly demonstrated through visual and vocal features rather than textual features. This result is consistent with study 1: according to the SHAP results, influencers' facial traits (such as eye gaze variation, length-to-width ratio, and age) and vocal traits (such as vocal energy and vocal variation) rank among the top traits, while traits extracted from text (such as intelligence, capability, expression efficiency, and skill) rank among the bottom traits.