# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 4 Research Methodology
### Feature Extraction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Feature** & **Type** & **Description** \\ \hline List count & Float & Number of lists that author participates in \\ \hline Description status & Boolean & Whether user provides a personal description \\ \hline Char-description length & Integer & Personal description length (in words) \\ \hline Word-description length & Integer & Personal description length (in char) \\ \hline User-favorite count & Integer & Number of posts that author favors \\ \hline Username length & Integer & Number of characters in username \\ \hline Screenname length & Integer & Number of characters in screenname \\ \hline User-followers count & Integer & Number of accounts that follow author \\ \hline User-friends count & Integer & Number of accounts that author follows \\ \hline Geo-enabled & Boolean & Whether account has enabled geographic location \\ \hline Media count & Integer & Number of media posted by author \\ \hline Custom timelines & Boolean & Whether user has a custom timeline \\ \hline Status count & Integer & Number of posts written by author account \\ \hline Verified & Boolean & Whether user has verified accounts \\ \hline URL status & Integer & Whether author account has a URL in homepage \\ \hline Protected & Boolean & Whether user has protected their tweets \\ \hline Consent status & Boolean & Whether account requires consent \\ \hline Can dm & Boolean & Whether account allows users to send direct messages privately \\ \hline Profile background tile & Boolean & Whether author account has background profiles tile \\ \hline Profile background image & Boolean & Whether author account has background profiles images \\ \hline Default profile & Boolean & Whether author account has not changed theme or background of profiles \\ \hline Default profile images & Boolean & Whether author account has changed the default profiles images \\ \hline User engagement & Float & User engagement (\# posts/(account age + 1)) \\ \hline Following rate & Float & Following rate (i.e., followings/(account age + 1)) \\ \hline Favorite rate & Float & Favorite rate (i.e., user favorites/(account age + 1)) \\ \hline User effects & Float & Whether the author is a producer or recipient determined by the formula \# followers/\# following \\ \hline Reputation score & Float & Reputation score of accounts, calculated by the formula \# followers/(\# followers+\# following + 1) \\ \hline Account age & Integer & Number of years since account creation \\ \hline Following & Boolean & Whether author account is followed by authenticated user \\ \hline Follow request sent & Boolean & Whether author account is requested to follow by authenticated user \\ \hline Notifications & Boolean & Whether author account has turned on notifications \\ \hline Contributor-enabled & Boolean & Whether account has enabled contributors \\ \hline Translation-enabled & Boolean & Whether account has enabled translations \\ \hline \hline \end{tabular}
\end{table} 
Table 3: User and content feature explanation. Sentiments and Emotions Features: Sentiments and emotional cues can be crucial in distinguishing between rumor and non-rumor. Estimating the emotional signals was the first step in our process. To circumvent the time-consuming annotation process for each response, we considered various public approaches. Two methods are employed for calculating emotional signals: the lexicon-based method and pre-trained models. The Arabic language is considered a low-resource language, and the available emotion-based lexicons suffer from a limited vocabulary size. To address this challenge, we used the Google API translator to translate text from Arabic into English, allowing us to utilize English lexicons to extract sentiments and emotions. There are two popular lexicons for emotion recognition:  
1.