Intelligent Disaster Response via Social Media Analysis A Survey <s> Trend Mining <s> We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend. We discuss the motivation for trend detection over social media streams and the challenges that lie therein. We then describe our approach to trend detection, as well as the architecture of TwitterMonitor. Finally, we lay out our demonstration scenario. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Trend Mining <s> Twitter, Facebook, and other related systems that we call social awareness streams are rapidly changing the information and communication dynamics of our society. These systems, where hundreds of millions of users share short messages in real time, expose the aggregate interests and attention of global and local communities. In particular, emerging temporal trends in these systems, especially those related to a single geographic area, are a significant and revealing source of information for, and about, a local community. This study makes two essential contributions for interpreting emerging temporal trends in these information systems. First, based on a large dataset of Twitter messages from one geographic area, we develop a taxonomy of the trends present in the data. Second, we identify important dimensions according to which trends can be categorized, as well as the key distinguishing features of trends that can be derived from their associated messages. We quantitatively examine the computed features for different categories of trends, and establish that significant differences can be detected across categories. Our study advances the understanding of trends on Twitter and other social awareness streams, which will enable powerful applications and activities, including user-driven real-time information services for local communities. © 2011 Wiley Periodicals, Inc. <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Trend Mining <s> Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hashtag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hashtag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hashtag popularity, which is mostly driven by exogenous factors. <s> BIB003
Mathioudakis et al. BIB001 define trends as "set of bursty keywords that occur frequently together" which are driven by events and breaking news. Naaman et al. BIB002 define a score for each word on Twitter and the top 30 words will be considered as trending in each hour. The score is calculated using ((word frequency in a specific hour)-(average frequency of the word in this specific hour across weeks))/(standard deviation of the frequency of this word in this hour across weeks). Trends on Twitter are either caused by an external happening (such as a natural disaster) or are specific to the social media (a tweet by a famous user). Based on the source, they are divided into exogenous and endogenous BIB002 . Trends indicate what are the major subjects that people talk about. Resources that the affected people need and the issued that they talk about can be a subset of trends after a disaster. TwitterMonitor BIB001 is a system that detect new trends/topics by finding bursty keywords and clustering them based on co-occurrence. In the analysis of trends, they summarize each trend using the most frequent keywords, other words which are not as dominant but they are highly correlated to the frequent keywords, and also the named entities and sources (URLs) which are mentioned frequently. Cui et al.  propose a clustering-based model for visualizing topic discovery and evolution. They use a heuristic to hash the topics in each time step and for discovering new topics and detecting the death of topics (these two are critical topics) they compare the hashes from one time step to the next. They also monitor the merge and split of topics and these changes in the topics are shown in a river flow visualization. Width of a flow shows the occurrence number of all involved keywords in that topics and the merge, split, birth, and death of topics are shown by colors. Another approach is a fine-grain classification of trends that is based on comparing the frequency of words/hashtags before and after a spike. Lehman et al. BIB003 classify the trends on Twitter into three categories based on the shape of the spike: activity centered before and during the peak, concentrated during and after the peak, and symmetric activity.