Influence Maximization in Social Networks: A Survey

Online social networks have become an important platform for people to communicate, share knowledge and disseminate information. Given the widespread usage of social media, individuals' ideas, preferences and behavior are often influenced by their peers or friends in the social networks that they participate in. Since the last decade, influence maximization (IM) problem has been extensively adopted to model the diffusion of innovations and ideas. The purpose of IM is to select a set of k seed nodes who can influence the most individuals in the network. In this survey, we present a systematical study over the researches and future directions with respect to IM problem. We review the information diffusion models and analyze a variety of algorithms for the classic IM algorithms. We propose a taxonomy for potential readers to understand the key techniques and challenges. We also organize the milestone works in time order such that the readers of this survey can experience the research roadmap in this field. Moreover, we also categorize other application-oriented IM studies and correspondingly study each of them. What's more, we list a series of open questions as the future directions for IM-related researches, where a potential reader of this survey can easily observe what should be done next in this field

Modeling Content Lifespan in Online Social Networks Using Data Mining

Online Social Networks (OSNs) are integrated into business, entertainment, politics, and education; they are integrated into nearly every facet of our everyday lives. They have played essential roles in milestones for humanity, such as the social revolutions in certain countries, to more day-to-day activities, such as streaming entertaining or educational materials. Not surprisingly, social networks are the subject of study, not only for computer scientists, but also for economists, sociologists, political scientists, and psychologists, among others. In this dissertation, we build a model that is used to classify content on the OSNs of Reddit, 4chan, Flickr, and YouTube according the types of lifespan their content have and the popularity tiers that the content reaches. The proposed model is evaluated using 10-fold cross-validation, using data mining techniques of Sequential Minimal Optimization (SMO), which is a support vector machine algorithm, Decision Table, Naïve Bayes, and Random Forest. The run times and accuracies are compared across OSNs, models, and data mining algorithms. The peak/death category of Reddit content can be classified with 64% accuracy. The peak/death category of 4Chan content can be classified with 76% accuracy. The peak/death category of Flickr content can classified with 65% accuracy. We also used 10-fold cross-validation to measure the accuracy in which the popularity tier of content can be classified. The popularity tier of content on Reddit can be classified with 84% accuracy. The popularity tier of content on 4chan can be classified with 70% accuracy. The popularity tier of content on Flickr can be classified with 66% accuracy. The popularity tier of content on YouTube can be classified with only 48% accuracy. Our experiments compared the runtimes and accuracy of SMO, Naïve Bayes, Decision Table, and Random Forest to classify the lifespan of content on Reddit, 4chan, and Flickr as well as classify the popularity tier of content on Reddit, 4chan, Flickr, and YouTube. The experimental results indicate that SMO is capable of outperforming the other algorithms in runtime across all OSNs. Decision Table has the longest observed runtimes, failing to complete analysis before system crashes in some cases. The statistical analysis indicates, with 95% confidence, there is no statistically significant difference in accuracy between the algorithms across all OSNs. Reddit content was shown, with 95% confidence, to be the OSN least likely to be misclassified. All other OSNs, were shown to have no statistically significant difference in terms of their content being more or less likely to be misclassified when compared pairwise with each other

Mining and Managing Large-Scale Temporal Graphs

Large-scale temporal graphs are everywhere in our daily life. From online social networks, mobile networks, brain networks to computer systems, entities in these large complex systems communicate with each other, and their interactions evolve over time. Unlike traditional graphs, temporal graphs are dynamic: both topologies and attributes on nodes/edges may change over time. On the one hand, the dynamics have inspired new applications that rely on mining and managing temporal graphs. On the other hand, the dynamics also raise new technical challenges. First, it is difficult to discover or retrieve knowledge from complex temporal graph data. Second, because of the extra time dimension, we also face new scalability problems. To address these new challenges, we need to develop new methods that model temporal information in graphs so that we can deliver useful knowledge, new queries with temporal and structural constraints where users can obtain the desired knowledge, and new algorithms that are cost-effective for both mining and management tasks.In this dissertation, we discuss our recent works on mining and managing large-scale temporal graphs.First, we investigate two mining problems, including node ranking and link prediction problems. In these works, temporal graphs are applied to model the data generated from computer systems and online social networks. We formulate data mining tasks that extract knowledge from temporal graphs. The discovered knowledge can help domain experts identify critical alerts in system monitoring applications and recover the complete traces for information propagation in online social networks. To address computation efficiency problems, we leverage the unique properties in temporal graphs to simplify mining processes. The resulting mining algorithms scale well with large-scale temporal graphs with millions of nodes and billions of edges. By experimental studies over real-life and synthetic data, we confirm the effectiveness and efficiency of our algorithms.Second, we focus on temporal graph management problems. In these study, temporal graphs are used to model datacenter networks, mobile networks, and subscription relationships between stream queries and data sources. We formulate graph queries to retrieve knowledge that supports applications in cloud service placement, information routing in mobile networks, and query assignment in stream processing system. We investigate three types of queries, including subgraph matching, temporal reachability, and graph partitioning. By utilizing the relatively stable components in these temporal graphs, we develop flexible data management techniques to enable fast query processing and handle graph dynamics. We evaluate the soundness of the proposed techniques by both real and synthetic data. Through these study, we have learned valuable lessons. For temporal graph mining, temporal dimension may not necessarily increase computation complexity; instead, it may reduce computation complexity if temporal information can be wisely utilized. For temporal graph management, temporal graphs may include relatively stable components in real applications, which can help us develop flexible data management techniques that enable fast query processing and handle dynamic changes in temporal graphs

Digital news report 2023

The twelfth Digital News Report from the Reuters Institute for the Study of Journalism at the University of Oxford reveals new insights about digital news consumption based on a YouGov survey of over 93,000 online news consumers in 46 media markets The report provides evidence that news audiences are becoming more dependent on digital and social platforms, putting further pressure on both ad-based and subscription business models of news organisations at a time when both household and company spending is being squeezed. The report documents how video-based content, distributed via networks such as TikTok, Instagram and YouTube are becoming more important for news, especially in parts of the Global South, while legacy platforms such as Facebook are losing influence. Both interest and trust in news continue to fall in many countries as the connection between journalism and much of the public continues to fray

Startup dilemmas - Strategic problems of early-stage platforms on the internet

siirretty Doriast

Saving New Sounds

"Over seventy-five million Americans listen to podcasts every month, and the average weekly listener spends over six hours tuning into podcasts from the more than thirty million podcast episodes currently available. Yet despite the excitement over podcasting, the sounds of podcasting’s nascent history are vulnerable and they remain mystifyingly difficult to research and preserve. Podcast feeds end abruptly, cease to be maintained, or become housed in proprietary databases, which are difficult to search with any rigor. Podcasts might seem to be highly available everywhere, but it’s necessary to preserve and analyze these resources now, or scholars will find themselves writing, researching, and thinking about a past they can’t fully see or hear. This collection gathers the expertise of leading and emerging scholars in podcasting and digital audio in order to take stock of podcasting’s recent history and imagine future directions for the format. Essays trace some of the less amplified histories of the format and offer discussions of some of the hurdles podcasting faces nearly twenty years into its existence. Using their experiences building and using the PodcastRE database—one of the largest publicly accessible databases for searching and researching podcasts—the volume editors and contributors reflect on how they, as media historians and cultural researchers, can best preserve podcasting’s booming audio cultures and the countless voices and perspectives podcasting adds to our collective soundscape.

Music Artists’ Online Streaming Business Strategies

Music artists’ incomes are directly affected by online music streaming technology advancements. A lack of business strategy, when faced with online music streaming, can lead to an inability to maintain a steady revenue stream among music artists. Rooted in the conceptual framework of disruptive innovation, the purpose of this qualitative multiple case study was to explore online streaming business strategies music artists use to maintain a steady revenue stream. Data were collected from semistructured interviews with six music artists in the New York City area who maintained a steady revenue stream in the past 5 years and focus group responses from a combination of four managers, engineers, or producers in the music industry in the New York City area who maintained a steady revenue stream in the past 5 years. Data were also collected from artists’ income reports. The themes that emerged from the thematic analysis were (a) marketing, (b) music distribution channels, (c) collaborations, and (d) live music performances. A recommendation for music artists is to build consumer-to-brand value relationships and value co-creation. The implications for positive social change include the potential to create sustainable income for music artists as small business owners and the development of more successful careers and derivative jobs within the music industry