Recently, Taiwan has been affected by the southwesterly airflow and tropical depression, which has caused continuous torrential rains and flood disasters have continued to spread throughout the country.
Recently, Taiwan has been affected by the southwesterly airflow and tropical depression, which has caused continuous torrential rains and flood disasters have continued to spread throughout the country. This time, the editor collected online news related to Typhoon Lu from 8/1-8/15, and analyzed it through the DiVoMiner text big data analysis platform, leading everyone to understand which topics and people were the main content of the news broadcast during the natural disaster.
First of all, we first understand the most frequently reported content during this typhoon. According to the subject classification, it can be divided into social aspects (disaster damage, civilian casualties, rescue operations, etc.), objective information (typhoon path, maximum rainfall, etc.), Policy announcements (relief payments, subsidies, policy orders, etc.). From the figure below, we find that the number of social faces is the largest, followed by objective data, and policy releases are the least. Compared with purely objective weather information analysis or policy announcements, news media prefer to report social news with discussion potential.
Next, let’s take a look at whether the news topics reported by the major media are different. As shown in the bar chart below, we can find that the major media news are all social news. The number of news is the most prominent. Among all the media, only the United News Network The number of news about policy announcements is more than objective information, and the rest of the media have more objective information than policy announcements. It can be seen that in the face of natural disasters, regardless of the original party color of the media, priority will be given to reporting social news that has a viewable picture during the natural disaster.
In addition to the shocking images of natural disasters, the interaction of politicians during the natural disasters is also a major focus of media coverage. We analyzed with Mayor Chen Qimai, President Su Zhenchang, and President Tsai Ing-wen, who have the most media reports, to understand whether politicians’ interactive news reports are more than individual activities. From the figure below, we find that the number of news stories from the three politicians mentioning other politicians exceeds the number of news about personal political activities. During the natural disasters, the media tended to report that politicians interacted with others, rather than simply announcing policy information or disaster investigation information.
Finally, analyze whether there is a difference between the amount of news about politicians’ interaction and the amount of news about personal activities due to news media? Also analyze the three politicians with the largest number of reports: President Su Zhenchang, Mayor Chen Qimai, and President Tsai Ing-wen. According to the figure below, the largest number of news reports are politicians interacting with others. Only the ETTV Dongsen News has the largest number of reports by Dean Su alone. Personal activity news Mayor Chen Qimai is widely welcomed by the major media. The number of news far exceeds that of President Cai and Dean Su. Only China Times and ETTV Dongsen News have the most personal activity news of President Cai and Dean Su. During the natural disasters, the news media prefer to report multiple political figures at the same time, and the amount of political news about personal activities varies from media to media.
Although the rain is slowing down, I still want to remind everyone here to pay attention to their own safety when going out, and don’t worry about family members at home.-Find the source of research questions firstname.lastname@example.orgSet as a first-hand look to learn about the latest research trends and analysis reports# Source Big Data # flooded # farmland loss # Lupit # content analysis tool # make research easier # large text data #DiVoMiner