text: In a disaster situation, first responders need to quickly acquire situational awareness and prioritize response based on the need, resources available and impact. Can they do this based on digital media such as Twitter alone, or newswire alone, or some combination of the two? We examine this question in the context of the 2015 Nepal Earthquakes. Because newswire articles are longer, effective summaries can be helpful in saving time yet giving key content. We evaluate the effectiveness of several unsupervised summarization techniques in capturing key content. We propose a method to link tweets written by the public and newswire articles, so that we can compare their key characteristics: timeliness, whether tweets appear earlier than their corresponding news articles, and content. A novel idea is to view relevant tweets as a summary of the matching news article and evaluate these summaries. Whenever possible, we present both quantitative and qualitative evaluations. One of our main findings is that tweets and newswire articles provide complementary perspectives that form a holistic view of the disaster situation.
  @cite_11: 
  @cite_27: 
  @cite_31: In the era of data-driven journalism, data analytics can deliver tools to support journalists in connecting to new and developing news stories, e.g., as echoed in micro-blogs such as Twitter, the new citizen-driven media. In this paper, we propose a framework for tracking and automatically connecting news articles to Twitter conversations as captured by Twitter hashtags. For example, such a system could alert journalists about news that get a lot of Twitter reaction, so that they can investigate those conversations for new developments in the story, promote their article to a set of interested consumers, or discover general sentiment towards the story. Mapping articles to appropriate hashtags is nevertheless very challenging, due to different language styles used in articles versus tweets, the streaming aspect of news and tweets, as well as the user behavior when marking certain tweet-terms as hashtags. As a case-study, we continuously track the RSS feeds of Irish Times news articles and a focused Twitter stream over a two months period, and present a system that assigns hashtags to each article, based on its Twitter echo. We propose a machine learning approach for classifying and ranking article-hashtag pairs. Our empirical study shows that our system delivers high precision for this task.
  @cite_10: As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day. Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload. Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well. However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem. In this paper we investigate semantic user modeling based on Twitter posts. We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities. We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way. A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web.
  @cite_25: News reporting has seen a shift toward fast-paced online reporting in new sources such as social media. Web Search engines that support a news vertical have historically relied upon articles published by major newswire providers when serving news-related queries. In this paper, we investigate to what extent real-time content from newswire, blogs, Twitter and Wikipedia sources are useful to return to the user in the current fast-paced news search setting. In particular, we perform a detailed user study using the emerging medium of crowdsourcing to determine when and where integrating news-related content from these various sources can better serve the user's news need. We sampled approximately 300 news-related search queries using Google Trends and Bitly data in real-time for two time periods. For these queries, we have crowdsourced workers compare Web search rankings for each, with similar rankings integrating real-time news content from sources such as Twitter or the blogosphere. Our results show that users exhibited a preference for rankings integrating newswire articles for only half of our queries, indicating that relying solely on newswire providers for news-related content is now insufficient. Moreover, our results show that users preferred rankings that integrate tweets more often than those that integrate newswire articles, showing the potential of using social media to better serve news queries.
  @cite_17: We present a framework for sentiment analysis on tweets related to news items. Given a set of tweets and news items, our framework classifies tweets as positive or negative and links them to the related news items. For the classification of tweets we use three of the most used machine learning methods, namely Naive Bayes, Complementary Naive Bayes, and Logistic Regression, and for linking tweets to news items, Natural Language Processing (NLP) techniques are used, including Zemberek NLP library for stemming and morphological analysis and then bag-of-words method for mapping. To test the framework, we collected 6000 tweets and labeled them manually to build a classifier for sentiment analysis. We considered tweets and news in Turkish language only in this work. Our results show that Naive Bayes performs well on classifying tweets in Turkish.
summary: In @cite_31 , a framework for connecting news articles to Twitter conversations is proposed using Local cosine similarity, global cosine similarity, local frequency of the hashtag and global frequency of the hashtag as the classification features extracted for each article-hashtag pair. The task of linking tweets with related news articles is studied in another paper to construct user profiles @cite_10 . The authors proposed two sets of strategies to find relevant news articles to each tweet in this paper. In addition to URL-based strategies, which is similar to the idea used in @cite_25 , they also proposed several content-based strategies that include computing the similarity between hashtag-based, entity-based and bag-of-word-based representations of tweets and news articles to discover the relation between them. In addition to user modeling, the tweet-news linking task has been employed in document summarization @cite_11 , sentiment analysis @cite_17 and event extraction @cite_27