Intelligent Disaster Response via Social Media Analysis A Survey <s> Meme Tracking <s> ThemeRiver/sup TM/ is a prototype system that visualizes thematic variations over time within a large collection of documents. The "river" flows from left to right through time, changing width to depict changes in thematic strength of temporally associated documents. Colored "currents" flowing within the river narrow or widen to indicate decreases or increases in the strength of an individual topic or a group of topics in the associated documents. The river is shown within the context of a timeline and a corresponding textual presentation of external events. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Meme Tracking <s> Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity. <s> BIB002
Memes are short units of text that act as the signature of a topic  . Harve et al. BIB001 introduce a visualization for thematic change in over time for a corpus of documents. Their method shows a "river" of themes that includes colored "currents". Each current is a theme and its width at a specific point of time shows its strength. When a current becomes wider, the topic or set of topics associated with it are more dominant in the corpus and as the color changes, the themes in the corpus are is changing. Leskovec et al.  detect and track memes (quotes) in news articles. They generate a graph in which each node is a meme and there is an edge from node i to j if the meme in node i is strictly shorter that j and the directed edit distance to j is less than one. This directed acyclic graph is then partitioned in a way that all the nodes in one cluster can be considered "belonging" either to a single long phrase or to a single collection of phrases. To analyze the evolution of memes in a corpus of news articles, they use variations of a meme and extract all the articles that contain those memes and graphical visualize the changes in the volume of corresponding documents over time using stacked plots. The same concept of memes has been used to monitor bias in news outlet by different parties in the United States. Niculae et al. BIB002 build a bipartite graph from quotes of the president's speeches to news outlets which are used in a matrix factorization method to predict the future quotes of a news outlet based on its previous quotes. Moreover, they analyze the sentiment and negativity of quotes in different outlets and present the bias present in reporting parts of the speeches with a specific choice of negative words and negative sentiment in one of the parties.