text: Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. Challenges come from the vast diversity of events and media, limited access to aligned datasets across different media and a great deal of noise in the datasets. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. We also propose three metrics to interpret event popularity trends between pairwise social platforms. Experimental results on eighteen real-world event datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering the knowledge of various aspects related to events and different media.
  @cite_14: 
  @cite_33: With the rapid development of Internet, new media, such as blogs, wikis, and social media, become a major platform for information dissemination. Numerous studies focus on event dissemination trend analysis for individual media platform, while very few works are conducted to study the dissemination characteristics in a cross-platform manner. In this work, we propose ESAP, a novel cross-platform approach to analyze the event dissemination trend between social network and search engine simultaneously. ESAP includes three models: an event popularity model based on hot word dynamic weight; a trend similarity model to measure the similarity of event popularity across different platforms over time; and an attention degree model to measure event public attention through time. Experimental results based on four real-world event dissemination datasets (two from Baidu and two from Weibo) produce several interesting findings and validate the effectiveness of ESAP in modeling and analyzing event dissemination trend between social network and search engine from different perspectives.
  @cite_8: Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.
  @cite_21: Online social streams such as Twitter Facebook timelines and forum discussions have emerged as prevalent channels for information dissemination. As these social streams surge quickly, information overload has become a huge problem. Existing keyword search engines on social streams like Twitter Search are not successful in overcoming the problem, because they merely return an overwhelming list of posts, with little aggregation or semantics. In this demo, we provide a new solution called by grouping posts into events, and track the evolution patterns of events as new posts stream in and old posts fade out. Noise and redundancy problems are effectively addressed in our system. Our demo supports refined keyword query on evolving events by allowing users to specify the time span and designated evolution pattern. For each event result, we provide various analytic views such as frequency curves, word clouds and GPS distributions. We deploy on real Twitter streams and the results show that our demo outperforms existing keyword search engines on both quality and usability.
  @cite_3: 
  @cite_23: Abstract Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets.
  @cite_2: We establish a novel task in the spirit of news summarization and topic detection and tracking (TDT): daily determination of the topics newly popular with Wikipedia readers. Central to this effort is a new public dataset consisting of the hourly page view statistics of all Wikipedia articles over the last three years. We give baseline results for the tasks of: discovering individual pages of interest, clustering these pages into coherent topics, and extracting the most relevant summarizing sentence for the reader. When compared to human judgements, our system shows the viability of this task, and opens the door to a range of exciting future work.
  @cite_31: The diffusion of breaking news largely relies on propagation behaviors in the media. The tremendous and intricate propagation relationships in the media form a complex network. An improved understanding of breaking news diffusion characteristics can be obtained through the complex network research. Drawing on the news data of Bohai Gulf oil spill event from June 2011 to May 2014, we constructed a weighted and directed complex network in which media are set as nodes, the propagation relationships as edges and the propagation times as the weight of the edges. The primary results show (1) the propagation network presents small world feature, which means relations among media are close and breaking news originating from any node can spread rapidly; (2) traditional media and official websites are the typical sources for news propagation, while business portals are news collectors and spreaders; (3) the propagation network is assortative and the group of core media facilities the spread of breaking news faster; (4) for online media, news originality factor become less important to propagation behaviors. This study offers a new insight to explore information dissemination from the perspective of statistical physics and is beneficial for utilizing the public opinion in a positive way.
  @cite_11: 
summary: Many researches @cite_2 @cite_3 @cite_21 @cite_11 have focused on event evolution analysis for a single medium. The event popularity was evaluated by hourly page view statistics from Wikipedia in @cite_2 . Reference @cite_21 chose the density-based clustering method to group the posts in social text streams into events and tracked the evolution patterns. Breaking news dissemination is studied via network theory based propagation behaviors in @cite_31 . Reference @cite_33 proposed a TF-IDF based approach to analyze event popularity trends. In all, network-based approaches usually have high computational complexity, while frequency-based methods are usually less accurate on reflecting the real event popularity. Reference @cite_23 proposed a keyword extraction method for tweet message collections. However, it missed some valuable semantic information, which could have contributed to better results. Reference @cite_14 presented a topic labeling model by learning semantic representations of topic words and clustering coherent topic labels. A context-sensitive method based on PageRank to extract topical keyphrases was proposed in @cite_8 . Their work sheds light on integrating lexical, contextual and semantic relations in our TF-SW model.