# Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
## 4 Experiments
### Experiment on User Influence Analysis  
Next, we conducted the experiments on user influence estimation. To evaluate the effectiveness of influence estimation, we assume that the users officially authenticated by Twitter are better-known which have higher visibility and thus higher influences. Thus, we define the coverage @ N as the percentage of Twitter authenticated users among the top N users discovered by user influence score calculated using different edge weighting. Since edge weighting reflect the relative importance of user actions, from our initial observations, we tested four different sets of edge weighting as in Table 3.  
As shown in Table 3, in order to determine the relative importance of different user actions such as \"posting\", \"sharing\", and \"retweeting\", we conducted preliminary experiments and obtained some observations on user participation actions in social media. First, the absolute number of tweets posted by a user can be a misleading factor to determine user influence, since there are many users who have a lot of tweets with almost no likes and shares. They also have very few followers. Therefore, they should not have high influence. From Test1 and Test2, we want to verify that the weight of \"posting\" behavior should not be higher than that of \"sharing\". Second, considering the \"retweeting\" action which might indicate the additional chances of exposure for the tweet to the public, they might be read and further spread, which make them influential. From Test3 and Test4, we further verify if higher weights of \"retweeting\" action can lead to better performance. Based on the four sets of edge weighting, we evaluate their performance as in Figure 6.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Test 1** & **Test 2** & **Test 3** & **Test 4** \\ \hline Posting & 0.3 & 0.7 & 0.2 & 0.3 \\ Posted & 0.4 & 0.4 & 0.2 & 0.2 \\ Sharing & 0.7 & 0.3 & 0.8 & 0.7 \\ Shared & 0.1 & 0.1 & 0.1 & 0.0 \\ Retweeting & 0.4 & 0.4 & 0.6 & 0.8 \\ Retweeted & 0.1 & 0.1 & 0.1 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four sets of edge weights for different user actions.  
Figure 6: The coverage rates for different sets of edge weighting.  
As shown in Figure 6, the best performance can be achieved with the edge weighting of Test1 since it can cover more Twitter authenticated users. To get better performance, the weighting of sharing should be higher than posting, but the weighting of posting cannot be too low. A small percentage of weighting for retweeted and shared can improve the accuracy. Also, since there might not be many retweets, retweeting weight should not be too high. To further verify the effect of influence score on finding the top users, we compared it with the number of followers, retweets, and favorites as in Figure 7.  
As shown in Figure 7, the number of followers is the most accurate since it's one of the major criteria for being selected as the Twitter authenticated users. The number of favorites is close to the influence score, but it's decreasing faster. If we further analyze the top 100 users ranked by influence score, 33 were not included as the Twitter authenticated users. We can see their high number of followers. We can observe that they have high influence scores but have yet to be included as authenticated users by Twitter. This can validate the effectiveness of our proposed influence estimation in finding high potential influencers that are not yet authenticated.