In previous work we reported that resources linked in tweets disappeared at the rate of 11 in the first year followed by 7.3 each year afterwards. We also found that in the first year 6.7 , and 14.6 in each subsequent year, of the resources were archived in public web archives. In this paper we revisit the same dataset of tweets and find that our prior model still holds and the calculated error for estimating percentages missing was about 4 , but we found the rate of archiving produced a higher error of about 11.5 . We also discovered that resources have disappeared from the archives themselves (7.89 ) as well as reappeared on the live web after being declared missing (6.54 ). We have also tested the availability of the tweets themselves and found that 10.34 have disappeared from the live web. To mitigate the loss of resources on the live web, we propose the use of a "tweet signature". Using the Topsy API, we extract the top five most frequent terms from the union of all tweets about a resource, and use these five terms as a query to Google. We found that using tweet signatures results in discovering replacement resources with 70+ textual similarity to the missing resource 41 of the time.
This in-depth analysis and study of the social media, its nature, the information dissemination patterns, and the user behavior and interaction paved the way for the researchers to have a better understanding of how the social media played a major role in narrating publicly significant events. These studies prove that user-generated content in social media is of crucial importance and can be considered the first draft of history. analyzed two natural hazard events (the Oklahoma grass fires and the red River floods in 2009) and how microblogging contributed in raising the situational awareness of the public @cite_5 . Starbird and Palen analyzed how the crowd interact with politically sensitive context regarding the Egyptian revolution of 2011 @cite_18 . in another study utilized collaborative filtering techniques for identifying social media users who are most likely to be on the ground during a mass disruption event @cite_0 . investigated weblogs to examine societ al interactions to a disaster over time and how they reflect the collective public view towards this disaster @cite_17 .
@cite_0 @cite_5 @cite_18 @cite_17
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Sentiment analysis refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writer’s attitude towards various topics into positive, negative or neutral categories. Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised for sentiment mining. Data such as web-postings, Tweets, videos, etc., all express opinions on various topics and events, offer immense opportunities to study and analyze human opinions and sentiment. In this chapter, we study the information published by individuals in social media in cases of natural disasters and emergencies and investigate if such information could be used by first responders to improve situational awareness and crisis management. In particular, we explore applications of sentiment analysis and demonstrate how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management. Such information can also be used to help assess the extent of the devastation and find people who are in specific need during an emergency situation. We first provide the formal definition of sentiment analysis in social media and cover traditional and the state-of-the-art approaches while highlighting contributions, shortcomings, and pitfalls due to the composition of online media streams. Next we discuss the relationship among social media, disaster relief and situational awareness and explain how social media is used in these contexts with the focus on sentiment analysis. In order to enable quick analysis of real-time geo-distributed data, we will detail applications of visual analytics with an emphasis on sentiment visualization . Finally, we conclude the chapter with a discussion of research challenges in sentiment analysis and its application in disaster relief. Social media are forms of information and communication technology disseminated through social interaction. Social media rely on peer-to-peer (P2P) networks that are collaborative, decentralized, and community driven. They transform people from content consumers into content producers. Popular networking sites such as MySpace, Facebook, Twitter, and Google are versions of social media that are most commonly used for connecting with friends, relatives, and employees. The role of social media in disaster management became galvanized during the world response to the 2010 Haiti earthquake. During the immediate aftermath, much of what people around the world were learning about the earthquake originated from social media sources. Social media became the new forum for collective intelligence, social convergence, and community activism. During the first 2 days following the earthquake, "texting" mobile phone users donated more than $5 million to the American Red Cross. Both public and private response agencies used Google Maps. Millions joined MySpace and Facebook discussion groups to share information, donate money, and offer comfort and support. Social media has also been described as "remarkably well organized, self correcting, accurate, and concentrated," calling into question the ingrained view of unidirectional, official-to-public information broadcasts. Social media may also offer potential psychological benefit for vulnerable populations gained through participation as stakeholders in the response. Disaster victims report a psychological need to contribute, and by doing so, they are better able to cope with their situation. Affected populations may gain resilience by replacing their helplessness with dignity, control, as well as personal and collective responsibility. However, widespread use of social media also involves several important challenges for disaster management. Although social media is growing rapidly, it remains less widespread and accessible than traditional media. Also, public officials often view P2P communications as "backchannels" with potential to spread misinformation and rumor. In addition, in absence of the normal checks and balances that regulate traditional media, privacy rights violations can occur as people use social media to describe personal events and circumstances. Language: en Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions. Situation awareness plays an important role in disaster response and emergency management. Displaying real-time location-based social media messages along with videos, pictures, and hashtags during a disaster event could help first responders improve their situation awareness . A geo-targeted event observation (Geo) Viewer was developed for monitoring real-time social media messages in target areas with four major functions: (1) real-time display of geo-tagged tweets within the target area; (2) interactive mapping functions; (3) spatial, text, and temporal search functions using keywords, spatial boundaries, or dates; and (4) manual labeling and text-tagging of messages. Different from traditional web GIS maps, the user interface design of GeoViewer provides the interactive display of multimedia content and maps. The front-end user interface to visualize and query tweets is built with open source programming libraries using server-side MongoDB. GeoViewer is built for assisting emergency responses and disaster management tasks by tracking disaster event impacts, recovery activities, and residents’ needs in the target region. Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We demonstrate a web service that tracks political memes in Twitter and helps detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We also present some cases of abusive behaviors uncovered by our service. Our web service is based on an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. Over the last several years political actors worldwide have begun harnessing the digital power of social bots — software programs designed to mimic human social media users on platforms like Facebook, Twitter, and Reddit. Increasingly, politicians, militaries, and government-contracted firms use these automated actors in online attempts to manipulate public opinion and disrupt organizational communication. Politicized social bots — here ‘political bots’ — are used to massively boost politicians’ follower levels on social media sites in attempts to generate false impressions of popularity. They are programmed to actively and automatically flood news streams with spam during political crises, elections, and conflicts in order to interrupt the efforts of activists and political dissidents who publicize and organize online. They are used by regimes to send out sophisticated computational propaganda. This paper conducts a content analysis of available media articles on political bots in order to build an event dataset of global political bot deployment that codes for usage, capability, and history. This information is then analyzed, generating a global outline of this phenomenon. This outline seeks to explain the variety of political bot-oriented strategies and presents details crucial to building understandings of these automated software actors in the humanities, social and computer sciences. Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification accuracy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strikes. Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create “cultural bridges,” or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research. We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques. Social media represent powerful tools of mass communication and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We analyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similarity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this conversation and what roles they played. We study how roles and individual influence evolved during the period of the upheaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly intertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior. Cursing is not uncommon during conversations in the physical world: 0.5 to 0.7 of all the words we speak are curse words, given that 1 of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people's cursing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform - Twitter, involving the analysis of about 51 million tweets and about 14 million users. In particular, we explore a set of questions that have been recognized as crucial for understanding cursing in offline communications by prior studies, including the ubiquity, utility, and contextual dependencies of cursing. Being able to automatically link social media information and data to remote-sensed data holds large possibilities for society and research. In this paper, we present a system called JORD that is able to autonomously collect social media data about technological and environmental disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. To show the capabilities of the system, we present some examples of disaster events detected by the system. To evaluate the quality of the provided information and usefulness of JORD from the potential users point of view we include a crowdsourced user study. In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach. Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as “slacktivists” because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a per capita basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior. Disaster-related research in human-centered computing has typically focused on the shorter-term, emergency period of a disaster event, whereas effects of some crises are long-term, lasting years. Social media archived on the Internet provides researchers the opportunity to examine societ al reactions to a disaster over time. In this paper we examine how blogs written during a protracted conflict might reflect a collective view of the event. The sheer amount of data originating from the Internet about a significant event poses a challenge to researchers; we employ topic modeling and pronoun analysis as methods to analyze such large-scale data. First, we discovered that blog war topics temporally tracked the actual, measurable violence in the society suggesting that blog content can be an indicator of the health or state of the affected population. We also found that people exhibited a collective identity when they blogged about war, as evidenced by a higher use of first-person plural pronouns compared to blogging on other topics. Blogging about daily life decreased as violence in the society increased; when violence waned, there was a resurgence of daily life topics, potentially illustrating how a society returns to normalcy. Text data such as online news and microblogs bear valuable insights regarding important events and responses to such events. Events are inherently temporal, evolving over time. Existing visual text analysis systems have provided temporal views of changes based on topical themes extracted from text data. But few have associated topical themes with events that cause the changes. In this paper, we propose an interactive visual analytics system, LeadLine, to automatically identify meaningful events in news and social media data and support exploration of the events. To characterize events, LeadLine integrates topic modeling, event detection, and named entity recognition techniques to automatically extract information regarding the investigative 4 Ws: who, what, when, and where for each event. To further support analysis of the text corpora through events, LeadLine allows users to interactively examine meaningful events using the 4 Ws to develop an understanding of how and why. Through representing large-scale text corpora in the form of meaningful events, LeadLine provides a concise summary of the corpora. LeadLine also supports the construction of simple narratives through the exploration of events. To demonstrate the efficacy of LeadLine in identifying events and supporting exploration, two case studies were conducted using news and social media data. Immediately following the Boston Marathon attacks, individuals near the scene posted a deluge of data to social media sites. Previous work has shown that these data can be leveraged to provide rapid insight during natural disasters, disease outbreaks and ongoing conflicts that can assist in the public health and medical response. Here, we examine and discuss the social media messages posted immediately after and around the Boston Marathon bombings, and find that specific keywords appear frequently prior to official public safety and news media reports. Individuals immediately adjacent to the explosions posted messages within minutes via Twitter which identify the location and specifics of events, demonstrating a role for social media in the early recognition and characterization of emergency events. *Christopher Cassa and Rumi Chunara contributed equally to this work. Language: en Analyzing unstructured text streams can be challenging. One popular approach is to isolate specific themes in the text, and to visualize the connections between them. Some existing systems, like ThemeRiver, provide a temporal view of changes in themes; other systems, like In-Spire, use clustering techniques to help an analyst identify the themes at a single point in time. Narratives combines both of these techniques; it uses a temporal axis to visualize ways that concepts have changed over time, and introduces several methods to explore how those concepts relate to each other. Narratives is designed to help the user place news stories in their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as weblogs), a user discovers both important events and measures the social relevance of these stories. Social media is being increasingly used to request information and help in situations like natural disasters, where time is a critical commodity. However, generic social media platforms are not explicitly designed for timely information seeking, making it difficult for users to obtain prompt responses. Algorithms to ensure prompt responders for questions in social media have to understand the factors affecting their response time. In this paper, we draw from sociological studies on information seeking and organizational behavior to model the future availability and past response behavior of the candidate responders. We integrate these criteria with their interests to identify users who can provide timely and relevant responses to questions posted in social media. We propose a learning algorithm to derive optimal rankings of responders for a given question. We present questions posted on Twitter as a form of information seeking activity in social media. Our experiments demonstrate that the proposed framework is useful in identifying timely and relevant responders for questions in social media. Abstract Social media data are increasingly being used for enhancing situational awareness and assisting disaster management. We analyzed the wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness. Findings show that social media data can characterize the wildfire across space and over time, and thus are applicable to provide useful information on disaster situations. Second, people have strong geographical awareness during wildfire hazards and are interested in communicating situational updates related to wildfire damage (e.g., containment percentage and burned acres), wildfire response (e.g., evacuation), and appreciation to firefighters. Third, news media and local authorities are opinion leaders and play a dominant role in the wildfire retweet network.