The last case is about a social media in China called Sina-weibo. As we known, social media can provide rapid and immediate real-time information about events.
In this case, we are going to talk about social media helps government to make the right decisions by analyzing citizen's weibo texts when an emergency occurs.
Sina-Weibo, is the most popular microblogging service in China. The unique characteristics of Sina-Weibo are as follows:
(1) Short, topical text messages: Different from other blogging sites, the length of Sina-Weibo text messages is limited to 140 words or less, and emoticons are allowed to express emotions.During emergency events, the concerns of different groups are often different.
(2) Time-sensitivity: the popularity of smart handheld mobile devices and the development of modern communication technology make it easier to publish one's thoughts and ideas via Sina-Weibo. When an emergency occurs, affected individuals usually post the information of the events to social networks immediately.
People in social networks can publish their concerns, views, or even suggestions about the events after seeing the information. The timely posting and discussions reflect how people are concerned with the events and, in many cases, also where these people are located.
(3) Location information: Sina-Weibo encourages users to share location information. By analyzing the Sina-Weibo published in 2013, we find that 6.656% of the total number of Sina-Weibo contains GPS information.
On 21-22 July 2012, Beijing suffered the strongest rainstorm and urban flooding in over 60 years. According to data released by the Beijing City Government, about 1.6 million people's normal daily lives were disrupted, some 10.6 thousand houses were destroyed and the economic loss was estimated to be around 11.64 billion yuan.
1. Initially, we used 79,723 Sina-Weibo texts that were captured between 00:00 a.m. on 20 July and 00:00 a.m. on 22 July as original data for constructing a list of message topics. Each Sina-Weibo text was pre-processed by a word segmentation procedure, which allowed individual words in the document be analyzed separately.
In addition, Sina-Weibo emoticons, which conveyed important semantic information, should be added to the dictionary for Chinese word segmentation. Then, stop words, which are composed by a pointless word, are removed.
2. Using topic model for the Sina-Weibo text after data pre-processing, we obtained two lists. One is Topic-Terminology lists, and the other is Document-Topic lists,
When a new Sina-Weibo text was acquired, we identified the category to which it belonged.
The Topic-Terminology lists showed the vocabularies of each topic included and the frequency of these vocabularies occurred. in the event of "Beijing rainstorm", we generalized some of the 40 topics into five topics ("traffic", "weather", "disaster information", "loss and influence", "rescue information").
The Document-Topic lists show the topics of each Sina-Weibo texts might belong and the probability of these topics. In this paper, for a Sina-Weibo text, if the greatest probability of belonging to a topic is greater than the three times of the average value, the Sina-Weibo text was considered to be belonging to this topic. Otherwise, the Sina-Weibo text would be considered to be belonging to none of these topics.
3. In regular time intervals, re-do the step (1) and the step (2), so that the emergency information classification model was adapted to new Sina-Weibo texts. In order to display Sina-Weibo texts by topics, we geotagged the Sina-Weibo that contain GPS information.
we implemented a prototype system that classified Sina-Weibo texts in the real-time and displayed the Sina-Weibo texts with GPS information on the map, by topics, as shown Figure.And based on Density analysis, we can create a possible spatial structure for distributing resources in response to emergencies by topics.