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US-based reporting jobs are increasingly concentrated within a small number of major metropolitan areas, driven by digital journalism outlets, according to research over the past few years from media analysts like Joshua Benton at Harvard’s Nieman Lab and Jack Shafer and Tucker Doherty of Politico. As for cities where journalism jobs still flourish, New York City is atop that list. According to a 2015 analysis by Jim Tankersley in The Washington Post, the number of reporting jobs in New York basically held steady in the years between 2004 and 2014, while the number of reporting jobs outside that city, Los Angeles, and Washington, D.C., dropped by 25 percent in the same time period. However, the proliferation of new, often unstable digital journalism hiring booms in the largest city in the US has masked just how dire the situation is for local reporting. Paul Moses illustrated this aptly in a 2017 piece for The Daily Beast, based on research for the CUNY Graduate School of Journalism’s Urban Reporting Program, highlighting a lack of any dedicated reporter covering Queens County courts (which would be the nation’s fourth largest city if it stood on its own). He wrote, “The problem for local news coverage is the simple fact that a story aimed at a national audience is likelier to generate heavy web traffic than a local one. Original local news reporting is threatened not only by layoffs but by the transfer of jobs to writing on whatever is of interest to a national web audience.” This common concern for the troubling state of local news in New York City led the Tow Center for Digital Journalism at Columbia University, the New York City Mayor’s Office of Media and Entertainment, and WNYC to convene an off-the-record roundtable discussion focused on The Future of Local News on February 9, 2018, at the Columbia University School of Journalism. The goal of the discussion was to bring together a select group of journalists, publishers, academics, funders, public-sector representatives, and other experts to discuss how to reverse the crisis in poorly resourced New York local media and work toward innovative solutions to ensure a sustainable future for local news. The half-day roundtable took place in the morning and comprised a closed discussion built around three major questions: 1) What is the state of local journalism in New York City at the beginning of 2018? 2) What trends and emerging business models in local news across the US and internationally might we be able to learn from? 3)Where do we go from here? What are possible futures for local media in New York?

The rise of digital media has fundamentally changed the relationship between marketers and publishers. As audiences increasingly move toward mobile consumption, publishers have had to adapt their business models based on new standards set by social media platforms and advertisers. They are now in competition with large tech companies to reach, and to own, the same audiences. The adtech ecosystem, which was designed by platforms and advertisers to capitalize on the growing amount of data retained about readers, has produced a mess of publishers’ main monetization strategies—leaving the entire space in dire need of evaluation and experimentation. On October 20, 2017, the Tow Center for Digital Journalism at Columbia University, the Digital Initiative at Harvard Business School, and the Shorenstein Center on Media, Politics, and Public Policy at the Harvard Kennedy School hosted a Policy Exchange Forum (PEF) and public conference to explore these shifts in “The Future of Advertising and Publishing.” The PEF took place in the morning and comprised a closed discussion (by invitation only) built around two major questions: What is the future of the relationship between publishers and advertisers? More specifically, how can platforms, news publishers, and advertisers ensure a robust future for news publishers by shaping the quality of advertising?

More than a quarter century after the creation of the World Wide Web, nine in ten Americans get at least some news online. But in many ways, local news publishing is still adapting to the internet as a news medium. For many publishers, the internet is like an ill-fitting suit: functional, but not made for them. These are some findings from a study of the digital footprint of more than 2,000 U.S. local news outlets. While many studies have explored digital transformation of newsrooms through direct interviews, case studies and ethnography, this report attempts to tell the story of that transformation by the numbers.1 The study also offers comparative perspective between various sectors of local media—including radio and television broadcast, daily and weekly print, digital-native publishers and collegiate press.

The history of journalism includes many and varied forms of cooperation, as far back as landmark events such as the creation of the Associated Press by five New York newspapers in 1846 to share costs related to the coverage of the Mexican-American War. What sets the current phase of collaboration apart from previous ones is the wide diffusion of networked forms of organization and production, and the transformative impact of these cooperative practices in reshaping the new media world and its under- lying social and technological infrastructure as public utilities. This report explores the gradual development of this phenomenon and the related development of a new commons for journalism, or a collection of shared resources and communities reconfiguring the material and cultural conditions of newswork as a social practice subject to dilemmas that require cooperation. The journalism commons, often going unrecognized in the academic and public discourse on the future of media, offers a framework to make sense of the new schemes of human relations, production, and governance.

The conventional wisdom of the digital era is that journalists can now know their audiences in far more intimate detail than at any other time in the history of the profession. Previously, journalists based their audience knowledge primarily on their closest social circles. Now, new tools can help them solicit readers’ feedback, analyze and understand readers’ behavior, and open new channels for conversation. These new capabilities promise to shine a light on the abstract audience—making one’s readers present, quantified and real. Drawing on the existing literature and an original case study, this paper asks whether the new tools of the digital age have indeed influenced the “audience in the mind’s eye.” Our evidence indicates that for the most part, they have not. In reviewing findings from the case study, we were struck by how little seems to have changed since the print era. Although they seemed more open to audience knowledge, the ways in which these reporters thought about their audiences was remarkably similar to those reported in classic ethnographies of the 1970s. The paper concludes with some hypotheses about why this may be so, and offers some possible approaches to improve audience awareness in the newsroom—in particular, a new perspective on the necessity (and difficulty) of diversity. It is our hope that this paper will inspire future research and experimentation—to narrow the gap between the audiences journalists have in mind and the audiences they serve.

Open source intelligence, which researchers and security services style OSINT, is one of the most valuable tools to a contemporary reporter, because of the vast amount of publicly available online information. Reporters conducting OSINT-based research should aspire to use the information they gather online to peer behind the superficial mask of the internet—the anonymous avatars on Twitter, for example, or the filtered photographs on Instagram—and tell the story of the real, flesh-and-blood human beings on the other side of our screens. Every time we go online, we give up part of our identity. Sometimes, it comes in the form of an email used to make a Twitter account. Other times, it’s a phone number for two-factor authentication, or days’ and weeks’ worth of timestamps suggesting when a user is awake and asleep. Journalists can piece together clues like this and use them to tell stories which are of interest to the public. The following guide is written to provide a basic foundation not only for doing that work, but also for verifying the information, archiving findings, and interacting with hostile communities online. The closer we get to understanding the people who make the influential and newsworthy aspects of the internet happen—and their motivations—the easier our work of discovery becomes.

Blockchain, like the internet, or democracy, or money, is many overlapping things. It is a decentralized record of cryptocurrency transactions. It is a peer-to-peer network of computers. It is an immutable, add-on-only database. What gets confusing is the way in which these overlapping functions override one definition or explanation of blockchain, only to replace it with an altogether different one. The conceptual overlaps are like glass lenses dropped on top of one another, scratching each other’s surface and confusing each other’s focal dimensions. This guide takes apart the stack of these conceptual lenses and addresses them one by one through the reconstruction of the basic elements of blockchain technology. The first section of this report gives a short history of blockchain, then describes its main functionality, distinguishing between private and public blockchains. Next, the guide breaks down the components and inner workings of a block and the blockchain. The following section focuses on blockchain’s journalistic applications, specifically by differentiating between targeted solutions that use blockchain to store important metadata journalists and media companies use on a daily basis, and hybrid solutions that include targeted solutions but introduce cryptocurrency, therein changing the journalistic business model altogether. Finally, the report speculates on the proliferation of what are known as Proof-of-Stake blockchain models, the spread of “smart contracts,” and the potential of enterprise-level and government-deployed blockchains, all in relation to what these mean to newsrooms and the work of reporters.

This research report explores archiving practices and policies across newspapers, magazines, wire services, and digital-only news producers, with the aim of identifying the current state of archiving and potential strategies for preserving content in an age of digital distribution. Between March 2018 and January 2019, we conducted interviews with 48 individuals from 30 news organizations and preservation initiatives. What we found was that the majority of news outlets had not given any thought to even basic strategies for preserving their digital content, and not one was properly saving a holistic record of what it produces. Of the 21 news organizations in our study, 19 were not taking any protective steps at all to archive their web output. The remaining two lacked formal strategies to ensure that their current practices have the kind of longevity to outlast changes in technology. Meanwhile, interviewees frequently (and mistakenly) equated digital backup and storage in Google Docs or content management systems as synonymous with archiving. (They are not the same; backup refers to making copies for data recovery in case of damage or loss, while archiving refers to long-term preservation, ensuring that records will still be available even as formatting and distribution technologies change in the future.) Instead, news organizations have handed over their responsibilities as public stewards to third-party organizations such as the Internet Archive, Google, Ancestry, and ProQuest, which store and distribute copies of news content on remote servers. As such, the news cycle now includes reliance on proprietary organizations with increasing control over the public record. The Internet Archive aside, the larger issue is that their incentives are neither journalistic nor archival, and may conflict with both. While there are a number of news archiving initiatives being developed by both individuals and nonprofits, it is worth noting that preserving digital content is not, first and foremost, a technical challenge. Rather, it’s a test of human decision-making and a matter of priority. The first step in tackling an archival process is the intention to save content. News organizations must get there. The findings of this study should be a wakeup call to an industry fond of claiming that democracy cannot be sustained without journalism, one which anchors its legitimacy on being a truth and accountability watchdog. In an era where journalism is already under attack, managing its record and future are as important as ever. Local, independent, and alternative news sources are especially at risk of not being preserved, threatening to leave critical exclusions in a record that will favor dominant versions of public history. As the sudden Gawker shutdown demonstrated in 2016, content can be confiscated and disappear instantly without archiving practices in place.

Native advertising is the central digital-revenue stream for the publishing industry. It makes up some 60 percent of the market, or $32.9 billion, according to a 2018 forecast by market research firm eMarketer. Understanding why the trend has been enthusiastically embraced by numerous news organizations requires a fuller appreciation of the changes that shape our information environment.

This dissertation examines the efforts of journalists to expand their pool of potential sources beyond a group of people often called "the usual suspects." This group consists of public officials, business leaders, experts, spokespeople, and other people who are in the news often. Using interviews, participant observation, a survey, and online ethnography, this research investigates how a growing skepticism of the usual suspects and increasingly powerful technology have led to innovations in the source search process. Some journalists have seen potential in digital search tools, including databases and social media, for finding sources that had once been too difficult or time-consuming to find. Journalists themselves have created two source-finding initiatives: a database called the Public Insight Network, and Storyful, which calls itself the "world's first social news agency." Storyful journalists specialize in finding and verifying social media content from the scenes of breaking news events. Journalists have also used other tools created by public relations professionals and technologists. How did the availability of these tools change the reporting process? It varied by tool, and by journalist. Although the tools were designed to do similar things, journalists used them in different ways. This dissertation examines how journalists used these tools in three stages of the reporting process: finding sources, verifying sources, and managing sources. Ultimately, most journalists used these tools not to find new sources, but to follow and research sources they had already identified by name or location. Few journalists had discovered new sources and story ideas with the help of digital search tools. So while these tools opened new possibilities for finding sources, journalists were still more likely to cover some people and topics over others.

This dissertation examines the emergence of a cohort of independent literary, intellectual, and political publications—“little magazines”—in New York City over the past decade. Helmed by web-savvy young editors, these publications have cultivated formidable reputations by grasping and capitalizing on a constellation of economic, political, and technological developments. The little magazines understand themselves as a radical alternative both to a journalistic trend toward facile, easily digestible content and to the perceived insularity and exclusivity of academic discourse. However, the bohemian tradition in which they operate predisposes them toward an insularity of their own. Their particular web of allusions, codes, and prerequisite knowledge can render them esoteric beyond the borders of a specific subculture and, in so doing, curtail their political potency and reproduce systems of privilege. This dissertation explores the tensions and limitations of the bohemian artist-activist ideal, and locates instances in which little magazines were able to successfully transcend subcultural boundaries to productively engage in a broader politics.

This project proceeds from a narrow question: What, if anything, is a brain wave? Beguiling in its simplicity, this question prompts a cultural-historical investigation that spans over 150 years of science, technology, and society. Proposed in 1869, the original theory of brain waves cites etheric undulations to explain reports of apparent thought transference. Though most modern thinkers no longer believe in outright telepathy, I argue that dreams of thought transmission and other mental miracles subtly persist—not in obscure and occult circles, but at the forefront of technoscience. A hybrid of science and fiction, brain waves represent an ideal subject through which to explore the ways in which technical language shrouds spiritual dreams. Today, the phrase “brain waves” often function as shorthand for electrical changes in the brain, particularly in the context of technologies that purport to “read” some aspect of mental function, or to transmit neural data to a digital device. While such technologies appear uniquely modern, the history of brain waves reveals that they are merely the millennial incarnation of a much older hope—a hope for transmission and transcendence via the brain’s emanations.

This dissertation examines the nature of the American digital public square in the 2010’s, a place where people learn about and come together to discuss matters of public concern. The newly digital public square is a key component of any functional democracy in the twenty-first century. The dissertation seeks to shed light, not only on the capacities of today’s news media institutions to produce and efficaciously distribute news and information and support a capacity for discussion and deliberation that provides a “public intelligence” on matters of concern, but also on the newly enlarged role of the public in new rituals of digestion of such news. The work draws upon multiple systems-focused analyses of the public square, interviews, and analyses of news production, the economics and dynamics facing those who both produce and distribute news, and the broader literature about and studies of the public square. Despite the manifest uncertainty regarding how journalism will be supported and the success of a politics where rhetoric is often untethered to the truth, a temptation still exists to see the changes to the public square in a piecemeal fashion and to assume the institutions, business models, and practices of the future will be minor modifications on or variations of the past. Much scholarship concludes that the patterns of decay and growth in this area will eventually generate equilibria in terms of press freedom, news production, news distribution, and engagement that are familiar, no less efficacious than, and only marginally distinct from those of the latter half of the twentieth century. In his book The Marketplace of Attention, Professor James Webster concludes that “the cultural ballast provided by the old media will remain with us,” and that polarizing forces will meet their match with the forces that concentrate public attention (Webster 2016). In contrast, this dissertation argues that the combination of forces acting upon the digital public square and its emergent dynamics in the late 2010s means it is already functioning in a qualitatively different manner than the largely analogue public square of the past and, as structured, it is increasingly failing to serve individuals, groups, communities, the public writ large, and most importantly our democratic processes. This argument is built on insights from my nearly a decade of work in the media reform community—specifically, from three systems analyses I developed leading the Public Square Program at the Democracy Fund of the dynamics surrounding civic engagement and the production of local news, the dynamics of audience attention, and public trust and press freedom. After making the case for the difference that already exists, the dissertation argues that, without engagement of a wide range of actors (civic, political, and commercial) in support of much-needed changes to institutions, along with policies that will support a renewal of civic media and a focus on new practices more appropriate for the rituals of the digitally and data-infused world we live in, it is entirely possible the public square will fail to adequately support democratic ends. The dissertation concludes with recommendations to avoid this outcome.

The relationship between technology platforms and news publishers has endured a fraught 18 months. Even so, the external forces of civic and regulatory pressure are hastening a convergence between the two fields at an accelerated rate beyond what we saw when we published our first report from this study, “The Platform Press: How Silicon Valley Reengineered Journalism,” in March 2017. Journalism has played a critical part in pushing for accountability into the practices of companies such as Facebook, Google and Twitter, yet newsrooms are increasingly oriented toward understanding and leveraging platforms as part of finding a sustainable future. In the latest phase of our multi-year research into the relationship between platforms and publishers, we found that despite negative rhetoric and sentiment in newsrooms toward technology companies, there is a rapid and ongoing merging in the functions of publishers and platforms, and an often surprisingly high level of involvement from platform companies in influencing news production.

A large amount of sensitive data is transferred, stored, processed, and analyzed daily in Online Social Networks (OSNs). Thus, an effective and efficient evaluation of the privacy level provided in such services is necessary to meet user expectations and comply with the requirement of the applicable laws and regulations. Several prior works have proposed mechanisms for evaluating and calculating privacy scores in OSNs. However, current models are system-specific and assess privacy only from the user's perspective. There is still a lack of a universal model that can quantify the level of privacy and compare between different systems. In this paper, we propose a generic framework to (i) guide the development of privacy metrics and (ii) to measure and assess the privacy level of OSNs, more specifically microblogging systems. The present study develops an algorithmic model to compute privacy scores based on the impact of privacy and security requirements, accessibility, and difficulty of information extraction. The proposed framework aims to provide users as well as system providers with a measure of how much the investigated system is protecting privacy. It allows also comparing the privacy protection level between different systems. The privacy score framework has been tested using real microblogging social networks and the results show the potential of the proposed privacy scoring framework.

Numerous studies have been conducted to extract relationships from different documents. However, extracting relationships from microblog posts is rarely studied. In this paper, we improve a novel kernel-based learning algorithm to mine the personae social relationships from microblog posts by combining the syntax and semantic meanings of the dependency trigram kernels (DTK). To deeply extract the personal social relationships of microblog posts, we define the relation feature words, provide seven rules for extracting these feature words, and propose a rule-based approach that mines these relation feature words from microblog posts. We construct relation feature word dictionaries for different relation types because of the lack of prominent relation features in microblog posts. We propose an algorithm to classify relation feature words by considering two features of the relation feature words, namely, syntax and semantic similarities between relation feature words in microblog posts and by using relation feature word dictionaries. Experimental results show that the average recall, precision, and F-measure of our proposed approach outperforms the original DTK in sentence selection, personae social relation extraction, and personae social relation classification. Finally, the relation graphs of five topics clarify that our proposed approach is effective for extracting personae social relations from microblog posts.

With the sustained development of social networks, increasing attention has been paid to social recommender systems. Current studies usually focus on indirect factors such as the similarity between users, but multiple direct interactions, such as mentions, reposts, and comments, are seldom considered. This paper addresses direct connections between users in social recommender systems. We analyze direct interactions to investigate the connection strength between users, and then, user preferences and item characteristics can be better described. Based on the analysis of social influence between users and users' influence over the whole social network, we propose a recommendation method with social influence, which makes full use of information among users in social networks and introduces the mechanisms of macroscopic and microscopic influences. Direct interactions between users are incorporated into a matrix factorization objective function. Real-world microblog data are applied to verify our model, and the results show that the proposed recommendation method outperforms other state-of-the-art recommendation algorithms.

With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more “cyber”conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).

In microblog networks, when a user posts a microblog, other users may forward the post, and then the forwarding process will bring about the rapid dissemination and diffusion of information. In this paper, we propose a comprehensive and novel approach to predict user forwarding behavior. Firstly, we build the feature sets that affect the microblog forwarding, such as interest topic,geographic location, user aggregation coefficient,neighborhood overlap and so on. These features are classified into four categories: user characteristics, microblog features, network structure features, and interactive behavior characteristics. Secondly, we establish a feature selection model based on Filtering and Wrapping for predicting the forwarding behavior of users. The model includes three aspects: (1)ANOVA(Analysis of variance): The value of each feature is analyzed by variance analysis. If the feature variance is small, the feature provides less information. (2)$\chi ^{2}$ test and point-two-column correlation analysis: They filter discrete and continuous features, respectively. (3)Wrapper analysis: In order to solve the strong correlations between the features, we use LVW(Las vagas wrapper) algorithm to analyze the above feature sets, and then obtain the optimal feature combination. Finally, we propose the forwarding prediction model based on AdaBoost(Adaptive boosting) algorithm. Experimental results demonstrate that the model has the highest precision and F1 score than Naive Bayes, Logistic Regression, Random Forest and SVM(Support vector machine), and the F1 score reached 0.885. Among different topics, our proposed AdaBoost prediction model has good recall and F1 scores for different topics. In addition, by using different feature sets for comparison experiments, it is found that the optimal features selected in this paper are very effective.

In recent years, microblog systems such as Twitter and Sina Weibo have averaged multimillion active users. On the other hand, the microblog system has become a new means of rumor-spreading platform. In this paper, we investigate the machine-learning-based rumor identification approaches. We observed that feature design and selection has a stronger impact on the rumor identification accuracy than the selection of machine-learning algorithms. Meanwhile, the rumor publishers' behavior may diverge from normal users', and a rumor post may have different responses from a normal post. However, mass behavior on rumor posts has not been explored adequately. Hence, we investigate rumor identification schemes by applying five new features based on users' behaviors, and combine the new features with the existing well-proved effective user behavior-based features, such as followers' comments and reposting, to predict whether a microblog post is a rumor. Experiment results on real-world data from Sina Weibo demonstrate the efficacy and efficiency of our proposed method and features. From the experiments, we conclude that the rumor detection based on mass behaviors is more effective than the detection based on microblogs' inherent features.

Online social networks (OSNs) are becoming more and more prevalent in people's life, but they face the problem of privacy leakage due to the centralized data management mechanism. The emergence of distributed OSNs (DOSNs) can solve this privacy issue, yet they bring inefficiencies in providing the main functionalities, such as access control and data availability. In this article, in view of the above-mentioned challenges encountered in OSNs and DOSNs, we exploit the emerging blockchain technique to design a new DOSN framework that integrates the advantages of both traditional centralized OSNs and DOSNs. By combining smart contracts, we use the blockchain as a trusted server to provide central control services. Meanwhile, we separate the storage services so that users have complete control over their data. In the experiment, we use real-world data sets to verify the effectiveness of the proposed framework.

The Berkman Center for Internet & Society at Harvard Law School is a research program founded to explore cyberspace, share in its study, and help pioneer its development.

Recently, Facebook has removed the "Switch to Classic Facebook" option from Settings. So here are four methods to switch back to classic FB.

This is the first installment of a multi-part series designed to help you familiarize yourself with the tools used to make visual, data-driven essays.

by John Perry Barlow Governments of the Industrial World, you weary giants of flesh and steel, I come from Cyberspace, the new home of Mind. On behalf of the future, I ask you of the past to leave us

NeoCities finds FCC's internal IP block, throttles connections to dial-up speed.

But NeoCities aims to be something bigger than fuck-yeah-90s retro.

GeoCities, the old free web hosting site from the 1990s, was amazingly ahead of its time. It did obsessiveness before Tumblr, self-infatuation before Facebook, and sorry Reddit, GeoCities straight-up ruled at GIFs. It wasn’t often pretty, but it was always interesting. But then Yahoo bought GeoCities, proceeded to run it into the ground, and eventually […]