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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 […]

Enthusiasts of ’90s web nostalgia say the real mission is bringing back simplicity and community, not dancing baby GIFs.

Quartz India is a guide to the world’s fastest-growing major economy. We provide in-depth coverage of the country for India and its far-flung diaspora.

A potential solution to the massive problem of linkrot.

IPFS isn’t exactly a well-known technology yet, even among many in the Valley, but it’s quickly spreading by word of mouth among folks in the open-source community. Many are excited by its potential to greatly improve file transfer and streaming speeds across the Internet. From my personal perspective, however, it’s actually much more important than that. IPFS eliminates the need for websites to have a central origin server, making it perhaps our best chance to entirely re-architect the Internet -- before its own internal contradictions unravel it from within.

The edges are smart, and what might seem weird today often becomes tomorrow’s norm.

Why old-school websites are the future of the internet.

Another day, another “making your own web site is nostalgia” article. The lengths I have gone to to try to dispell this running theme…

A generation too young to remember dial-up modems rediscovers the old, weird Internet.