On Trends Analysis And Prediction Based On Micro-Blogging Platforms

SNS (Social Networking Services) rise rapidly in recent years, and graduallypenetrate into the user groups all over the world. Microblogging is one of theimportant applications and have been rapidly developed in the last few years. Highcoverage, timeliness of content production and dissemination of information make themicroblogging platform a major news media. Huge number of users on the platformand the mass content, provide effective corpus for information mining in groups ofusers. This thesis attempts to extract various features of the data in the massive textresources of the microblogging platform, and calculate and analyze the trend of eventswhich is also in social computing area and difficult to quantify in the traditionalresearch. We model the trend according to the sample data, and predict the futuretrend according to the data outside the scope. This thesis is motivated to descript thepossibilities of computing the social contents.In this paper, we discussed several key issues of the event trend analysis andprediction on the Weibo platform. Including the calculation of group behavior;regression analysis of bursty event trends and future trends modeling; recognition andacquisition of event-related microblogging content; user characteristics and the text ofthe blog features extraction in microblogging platform, as well as the formaldefinition of the event trends. Main research and work results are summarized asfollows:1. Present a social computing framework based on group behavior. Within thisframework, we first define the indexes of ursers fetures. And then we have awhole portrait of massive users features. Thus to quantify the groups of users.Experiments results show the possibilities of group features computing.2. Present the framework of event trends analysis and prediction, and the methods indetail. Based on the sample data, we calculate the data value of each trend index.Thus we have a sample-based regression model. Then we calculate the futuretrend by the fusion model. Results show that it is a good way to aid the artificialdecision. Besides, there is little different between the absolute number ofpredictive data and actual data. Results of emotional proportion data also have a relative value.3. Present an event extraction method. This method combines the MACD algorithm(Moving Average Convergence and Divergence) and LDA algorithm (LatentDirichlet Allocation), and they are assigned to find the emergencies conten andrelated words of the known events expansion. By MACD algorithm, we calculatethe term frequency change of the unit time slice in the text of the microblogging,the use of aggregation and separation between the short-period moving averageline and long-period moving average line to recognize the burst content. The LDAalgorithm is used to calculate the event-related content of the "word bag" andrelated words in the event weight. Experimental results show that it is an effectmethod to extract the key content.4. Present a set of the formal definition of related content on the microbloggingplatform, including the platform, the users' network, user data, and data itemsinvolved to the platform. We also present a feature recognition method to classifythe users, simple but effective. All the formal definitions support well for thecalculation and analysis of the study.