In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to better understand how users react to trusted and deceptive news sources across two popular, and very different, social media platforms. To that end, (1) we develop a model to classify user reactions into one of nine types, such as answer, elaboration, and question, etc, and (2) we measure the speed and the type of reaction for trusted and deceptive news sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there are significant differences in the speed and the type of reactions between trusted and deceptive news sources on Twitter, but far smaller differences on Reddit.
As we noted above, most studies that examine misinformation spread focus on individual events such as natural disasters @cite_15 , political elections @cite_11 , or crises @cite_16 and examine the response to the event on social media. A recent study by Vosoughi al found that news stories that were fact-checked and found to be false spread faster and to more people than news items found to be true. In contrast, our methodology considers immediate reactions to news of varying credibility, so we can determine whether certain reactions or reactions to trusted or deceptive news sources evoke more or faster responses from social media users.
@cite_15 @cite_16 @cite_11
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We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise 126,000 stories tweeted by 3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98 agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it. The prevalence of misinformation within social media and online communities can undermine public security and distract attention from important issues. Fact-checking interventions, in which users cite fact-checking websites such as Snopes.com and FactCheck.org, are a strategy users can employ to refute false claims made by their peers. While laboratory research suggests such interventions are not effective in persuading people to abandon false ideas, little work considers how such interventions are actually deployed in real-world conversations. Using approximately 1,600 interventions observed on Twitter between 2012 and 2013, we examine the contexts and consequences of fact-checking interventions.W e focus in particular on the social relationship between the individual who issues the fact-check and the individual whose facts are challenged. Our results indicate that though fact-checking interventions are most commonly issued by strangers, they are more likely to draw user attention and responses when they come from friends. Finally, we discuss implications for designing more effective interventions against misinformation. The computer science research community has became increasingly interested in the study of social media due to their pervasiveness in the everyday life of millions of individuals. Methodological questions and technical challenges abound as more and more data from social platforms become available for analysis. This data deluge not only yields the unprecedented opportunity to unravel questions about online individuals' behavior at scale, but also allows to explore the potential perils that the massive adoption of social media brings to our society. These communication channels provide plenty of incentives (both economical and social) and opportunities for abuse. As social media activity became increasingly intertwined with the events in the offline world, individuals and organizations have found ways to exploit these platforms to spread misinformation, to attack and smear others, or to deceive and manipulate. During crises, social media have been effectively used for emergency response, but fear-mongering actions have also triggered mass hysteria and panic. Criminal gangs and terrorist organizations like ISIS adopt social media for propaganda and recruitment. Synthetic activity and social bots have been used to coordinate orchestrated astroturf campaigns, to manipulate political elections and the stock market. The lack of effective content verification systems on many of these platforms, including Twitter and Facebook, rises concerns when younger users become exposed to cyber-bulling, harassment, or hate speech, inducing risks like depression and suicide. This article illustrates some of the recent advances facing these issues and discusses what it remains to be done, including the challenges to address in the future to make social media a more useful and accessible, safer and healthier environment for all users. 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. The paper explores the use of concepts in cognitive psychology to evaluate the spread of misinformation, disinformation and propaganda in online social networks. Analysing online social networks to identify metrics to infer cues of deception will enable us to measure diffusion of misinformation. The cognitive process involved in the decision to spread information involves answering four main questions viz consistency of message, coherency of message, credibility of source and general acceptability of message. We have used the cues of deception to analyse these questions to obtain solutions for preventing the spread of misinformation. We have proposed an algorithm to effectively detect deliberate spread of false information which would enable users to make informed decisions while spreading information in social networks. The computationally efficient algorithm uses the collaborative filtering property of social networks to measure the credibility of sources of information as well as quality of news items. The validation of the proposed methodology has been done on the online social network Twitter’. The use of social media to communicate timely information during crisis situations has become a common practice in recent years. In particular, the one-to-many nature of Twitter has created an opportunity for stakeholders to disseminate crisis-relevant messages, and to access vast amounts of information they may not otherwise have. Our goal is to understand what affected populations, response agencies and other stakeholders can expect-and not expect-from these data in various types of disaster situations. Anecdotal evidence suggests that different types of crises elicit different reactions from Twitter users, but we have yet to see whether this is in fact the case. In this paper, we investigate several crises-including natural hazards and human-induced disasters-in a systematic manner and with a consistent methodology. This leads to insights about the prevalence of different information types and sources across a variety of crisis situations. The extensive use of social media has tremendous impact on culture, business, and politics on the world at large with potentially positive and negative effects. For example, social media coverage of crisis events may be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news for financial or political benefit. Considering the harmful consequences of fake news in social media, there is a profound need to detect false information, control and or prevent it from spreading. In this paper, we propose an advanced framework to identify tweets with fake news contents using techniques including statistical analysis of Twitter user account, reverse image searching, cross verification of fake news sources, and data mining. Experimental results on a large miscellaneous events dataset demonstrate the effectiveness of our proposed approach in identifying fake tweets. 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 As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim. The massive spread of fake news has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of digital misinformation and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots play a key role in the spread of fake news. Accounts that actively spread misinformation are significantly more likely to be bots. Automated accounts are particularly active in the early spreading phases of viral claims, and tend to target influential users. Humans are vulnerable to this manipulation, retweeting bots who post false news. Successful sources of false and biased claims are heavily supported by social bots. These results suggests that curbing social bots may be an effective strategy for mitigating the spread of online misinformation. This paper presents the first criminological analysis of an online social reaction to a crime event of national significance, in particular the detection and propagation of cyberhate on social media following a terrorist attack. We take the Woolwich, London terrorist attack in 2013 as our event of interest and draw on Cohen’s process of warning, impact, inventory and reaction to delineate a sequence of incidents that come to constitute a series of deviant responses following the attack. This paper adds to contemporary debates in criminology and the study of hate crime in three ways: (1) it provides the first analysis of the escalation, duration, diffusion and de-escalation of cyberhate in social media following a terrorist event; (2) it applies Cohen’s work on action, reaction and amplification and the role of the traditional media to the online context and (3) it introduces and provides a case study in ‘computational criminology’. The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size. Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks. In recent years, there has been a growing trend to use publicly available social media sources within the field of journalism. Breaking news has tight reporting deadlines, measured in minutes not days, but content must still be checked and rumors verified. As such, journalists are looking at automated content analysis to prefilter large volumes of social media content prior to manual verification. This article describes a real-time social media analytics framework for journalists. We extend our previously published geoparsing approach to improve its scalability and efficiency. We develop and evaluate a novel approach to geosemantic feature extraction, classifying evidence in terms of situatedness, timeliness, confirmation, and validity. Our approach works for new unseen news topics. We report results from four experiments using five Twitter datasets crawled during different English-language news events. One of our datasets is the standard TREC 2012 microblog corpus. Our classification results are promising, with F1 scores varying by class from 0.64 to 0.92 for unseen event types. We lastly report results from two case studies during real-world news stories, showcasing different ways our system can assist journalists filter and cross-check content as they examine the trust and veracity of content and sources. For over a generation, social scientists have tried to categorize the relationship between journalists and politicians. Which side holds power and influence over the other? Some scholars propose “active” theories: journalists have preferences and the power to impose them on anyone seeking media attention. Other scholars argue journalists are essentially “reactive,” dutifully writing down what politicians say with little ability to add alternate perspectives. In this dissertation, I propose both camps are extremes based on a fundamental misunderstanding of how journalists can apply their preferences on news content. Politicians and other sources provide information to reporters, bloggers and other new media writers. Each writer then chooses how to respond to this information. Journalistic power – whether we are discussing traditional media outlets or newer partisan media organizations – is best understood as a set of if : then propositions.The empirical sections of the dissertation consist of three separate studies, each of which focuses on one set of inputs and the output from a particular set of news organizations. The first study focuses on how presidents schedule press conferences at particular times and places. I find scheduling influences how much attention journalists give a conference, which in turn influences the balance of opinion found in stories. The second study shows how journalists resist but may ultimately give in to evasive responses, by examining quotations on a statement-by-statement basis. The third study examines some of the most popular phrases from the 2008 election, comparing how a wide range of media organizations responded to the same set of political and non-political ideas. Put together, these studies offer a common theoretical framework for comparing traditional and new media organizations, allowing for commonalities as well as differences. This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories. 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 In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the WebSci'10 conference. Among its findings were the discovery and documentation of what was labeled a "Twitter bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can in- crease their impact on real-world societ al issues. This paper examines how â officialâ accounts participate in the propagation and correction of online rumors in the context of crisis events. Using an emerging method for interpretive analysis of â bigâ social data, we investigate the spread of online rumors through digital tracesâ in this case, tweets. Our study suggests that official accounts can help to slow the spread of a rumor by posting a denial, andâ supported by reflections from an organization that recently dealt with a rumor-crisisâ offers best practices for organizations around social media strategies and protocols. Based on tweet data and connections to existing literature, we also demonstrate and discuss how mainstream media participate in rumoring, and note the role of a new breed of online media, â breaking newsâ accounts. This analysis offers a complementary perspective to existing studies that use surveys and interviews to characterize the role official accounts play in online rumoring. The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby's murder to train and test a supervised machine learning text classifier that distinguishes between hateful and or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.