## 3 Methods and Modeling
### Data Filtering and Feature Extraction  
On the basis of collected data and obtained keyword lists (including synonyms), the rule-based filtering strategy were utilized. Specifically, all the messages that either contain the words in the traffic keyword list or the words in the adverse weather keyword list were regarded as the related messages. Moreover, all tweet authors with fewer than 10 followers were regarded as spammers, and their corresponding messages were also filtered out as spam (false data).  
The data relevance has been ensured through the data collection and filtering steps. Then, the temporal features (date, season, holiday, weekday, etc.), location features (longitude, latitude, urban, countryside, etc.) and the meteorological features (including social features, such as different focus rate of each type of adverse weather, and also the real meteorological data like temperature, humidity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Warning Levels** & **Example words** \\ \hline \hline
**(Snow Storm)** & I, II, III and IV & **(Snow storm), **(S)**(S)**(blizzard), **(S)**(buran) \\ \hline \hline
**(Cold Wave)** & I, II, III and IV & **(S)**(cold wave), **(S)**(cold surge) \\ \hline \hline
**(Gale)** & I, II, III and IV & **(Gale), **(I)**(firee wind), **(I)**(firee wind), **(I)**(firee wind) \\ \hline \hline
**(Sand Storm)** & I, II and III & **(sand storm), **(S)**(desert storm), **(S)**(dust storm) \\ \hline
**(Heat Wave)** & I, II and III & **(heat wave), **(S)**(hight temperature), **(S)**(suana weather), **(S)**(canicule), **(I)**(dog days), **(S)**(blazing) \\ \hline
**(Drought)** & I and II & **(drought), **(S)**(aridity) \\ \hline
**(Lightning)** & I, II and III & **(S)**(lighting), **(S)**(hunder), **(S)**(hunder), **(S)**(hunder and lightning) \\ \hline
**(Hail)** & I and II & **(hail), **(S)**(hail fall), **(S)**(hail fall), **(S)**(hailstorm) \\ \hline
**(Frost)** & II, III and IV & **(frost), **(frost descent)**(frost descent) \\ \hline
**(Heavy Fog)** & I, II and III & **(heavy fog), **(S)**(thick fog), **(S)**(mist) \\ \hline
**(Haze)** & II and III & **(haze), **(S)**(smog) \\ \hline
**(Road** & I, II and III & **(road icing), **(I)**(icy road), **(S)**(road icing) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._and sea level pressure) that may potentially influence the traffic situation in both the real world and cyberspace were extracted.  
First, the temporal features were directly obtained from the tweets and converted to a timestamp format, and then mapped to other temporal features, such as season, holiday, and weekday. Second, we analyzed the contents of the tweets to resolve location features [34]. The locations were extracted from one tweet according to word frequency and looked up in the place name database to transfer the information to the prefecture-level city to which it belonged [50]. Third, the adverse weather types and traffic incidents were extracted based on the abovementioned word list. Finally, based on traffic word embedding, we aggregated all the tweets into city-level traffic incidents with retweet number as their heat or focus rate [51].  
After data collection and preprocessing, the features and labels are obtained, and thus we can estimate the traffic situation for cities and countries through the process of \"adverse weather tweets \(\rightarrow\) temporal, spatial, meteorological features \(\rightarrow\) traffic regression models \(\rightarrow\) traffic incidents heat \(\rightarrow\) alerting models \(\rightarrow\) traffic warning level.\" The regression model and alerting model will be discussed in the next section.