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Digital Platform As a Double-Edged Sword: How to Interpret Cultural Flows in the Platform Era
International Journal of Communication 11(2017), 3880–3898 1932–8036/20170005 Digital Platform as a Double-Edged Sword: How to Interpret Cultural Flows in the Platform Era DAL YONG JIN Simon Fraser University, Canada This article critically examines the main characteristics of cultural flows in the era of digital platforms. By focusing on the increasing role of digital platforms during the Korean Wave (referring to the rapid growth of local popular culture and its global penetration starting in the late 1990s), it first analyzes whether digital platforms as new outlets for popular culture have changed traditional notions of cultural flows—the forms of the export and import of popular culture mainly from Western countries to non-Western countries. Second, it maps out whether platform-driven cultural flows have resolved existing global imbalances in cultural flows. Third, it analyzes whether digital platforms themselves have intensified disparities between Western and non- Western countries. In other words, it interprets whether digital platforms have deepened asymmetrical power relations between a few Western countries (in particular, the United States) and non-Western countries. Keywords: digital platforms, cultural flows, globalization, social media, asymmetrical power relations Cultural flows have been some of the most significant issues in globalization and media studies since the early 20th century. From television programs to films, and from popular music to video games, cultural flows as a form of the export and import of cultural materials have been increasing. Global fans of popular culture used to enjoy films, television programs, and music by either purchasing DVDs and CDs or watching them on traditional media, including television and on the big screen. -
M&A @ Facebook: Strategy, Themes and Drivers
A Work Project, presented as part of the requirements for the Award of a Master Degree in Finance from NOVA – School of Business and Economics M&A @ FACEBOOK: STRATEGY, THEMES AND DRIVERS TOMÁS BRANCO GONÇALVES STUDENT NUMBER 3200 A Project carried out on the Masters in Finance Program, under the supervision of: Professor Pedro Carvalho January 2018 Abstract Most deals are motivated by the recognition of a strategic threat or opportunity in the firm’s competitive arena. These deals seek to improve the firm’s competitive position or even obtain resources and new capabilities that are vital to future prosperity, and improve the firm’s agility. The purpose of this work project is to make an analysis on Facebook’s acquisitions’ strategy going through the key acquisitions in the company’s history. More than understanding the economics of its most relevant acquisitions, the main research is aimed at understanding the strategic view and key drivers behind them, and trying to set a pattern through hypotheses testing, always bearing in mind the following question: Why does Facebook acquire emerging companies instead of replicating their key success factors? Keywords Facebook; Acquisitions; Strategy; M&A Drivers “The biggest risk is not taking any risk... In a world that is changing really quickly, the only strategy that is guaranteed to fail is not taking risks.” Mark Zuckerberg, founder and CEO of Facebook 2 Literature Review M&A activity has had peaks throughout the course of history and different key industry-related drivers triggered that same activity (Sudarsanam, 2003). Historically, the appearance of the first mergers and acquisitions coincides with the existence of the first companies and, since then, in the US market, there have been five major waves of M&A activity (as summarized by T.J.A. -
A Survey of Adversarial Learning on Graph
A Survey of Adversarial Learning on Graph LIANG CHEN, Sun Yat-sen University of China JINTANG LI, Sun Yat-sen University of China JIAYING PENG, Sun Yat-sen University of China TAO XIE, Sun Yat-sen University of China ZENGXU CAO, Hangzhou Dianzi University of China KUN XU, Sun Yat-sen University of China XIANGNAN HE, University of Science and Technology of China ZIBIN ZHENG∗, Sun Yat-sen University of China Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classication, link prediction and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies have emerged for both aack and defense addressed in dierent graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unied problem denition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specically, we survey and unify the existing works w.r.t. aack and defense in graph analysis tasks, and give appropriate denitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and oer insights for the relevant researchers. More details of our works are available at hps://github.com/gitgiter/Graph-Adversarial-Learning. CCS Concepts: •Computing methodologies ! Semi-supervised learning settings; Neural networks; •Security and privacy ! So ware and application security; •Networks ! Network reliability; Additional Key Words and Phrases: adversarial learning, graph neural networks, adversarial aack and defense, adversarial examples, network robustness ACM Reference format: Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng. -
Citron Research Backing up the Sleigh on Facebook – 2019 S&P
December 26, 2018 Citron Research Backing Up the Sleigh on Facebook – 2019 S&P Stock of the Year Separating the Noise from Reality will take Facebook back to $160 Two and a half years ago, Citron Research said that FB was a long-term short and that engagement levels would eventually top out. At the time, the stock was trading in the $120 range. In the past 30 months FB has more than doubled its quarterly revenue and concerns of engagement have shifted to concerns of addiction, yet the stock is back down in $120 range. Time to back up the sleigh! Yet, 2018 is a year that Facebook shareholders would like to forget, as the stock is down 30% YTD and over 40% from its recent high. Yet, while the media has reported on one scandal after the next and the NYT even tried to promote a #deletefacebook movement. The truth is revenues and more important user base has seen little impact. As you read this article Facebook has 2.2 billion active users and has grown revenues 33% q over q during this controversial 12 months. Considering the user base excludes China, this is a lead that has no second place. Going Forward There are only three talking points on Facebook that matter: 1. Putting Facebook Valuation in Perspective 2. Is Facebook Evil or Investible? 3. Instagram – the Anti CRaP experience © Copyright 2018 | Citron Research | www.citronresearch.com | All Inquiries – [email protected] December 26, 2018 Valuation in Perspective Facebook is growing faster than 95% of the S&P with margins higher than about 90% of the S&P. -
Facebook Timeline
Facebook Timeline 2003 October • Mark Zuckerberg releases Facemash, the predecessor to Facebook. It was described as a Harvard University version of Hot or Not. 2004 January • Zuckerberg begins writing Facebook. • Zuckerberg registers thefacebook.com domain. February • Zuckerberg launches Facebook on February 4. 650 Harvard students joined thefacebook.com in the first week of launch. March • Facebook expands to MIT, Boston University, Boston College, Northeastern University, Stanford University, Dartmouth College, Columbia University, and Yale University. April • Zuckerberg, Dustin Moskovitz, and Eduardo Saverin form Thefacebook.com LLC, a partnership. June • Facebook receives its first investment from PayPal co-founder Peter Thiel for US$500,000. • Facebook incorporates into a new company, and Napster co-founder Sean Parker becomes its president. • Facebook moves its base of operations to Palo Alto, California. N. Lee, Facebook Nation, DOI: 10.1007/978-1-4614-5308-6, 211 Ó Springer Science+Business Media New York 2013 212 Facebook Timeline August • To compete with growing campus-only service i2hub, Zuckerberg launches Wirehog. It is a precursor to Facebook Platform applications. September • ConnectU files a lawsuit against Zuckerberg and other Facebook founders, resulting in a $65 million settlement. October • Maurice Werdegar of WTI Partner provides Facebook a $300,000 three-year credit line. December • Facebook achieves its one millionth registered user. 2005 February • Maurice Werdegar of WTI Partner provides Facebook a second $300,000 credit line and a $25,000 equity investment. April • Venture capital firm Accel Partners invests $12.7 million into Facebook. Accel’s partner and President Jim Breyer also puts up $1 million of his own money. -
Graph Search Process in Social Networks and It's Challenges
Preeti Narooka et al | IJCSET(www.ijcset.net) | June 2016 | Vol 6, Issue 6, 228-232 Graph Search Process in Social Networks and it’s Challenges Ms. Preeti Narooka1, Dr. Sunita Chaodhary2 Ph. D Student, Banasthali University, Jaipur, India1 Former Associate Professor, Banasthali University, Jaipur, India 2 Abstract: - Social networking sites are grabbing attention of complex network where huge amount of data can be millions of users and it resulted into creation of humongous distribute and store easily. data on these networking sites. Number of users on any networking sites denotes the complexity and size of the data. Graph algorithm is the efficient tool which defines the These developments have thrown a challenge in front of relationships between all objects on social cloud. In today’s computer scientists to optimize the search operations on these scenario, users are increasing in Social Networks in leaps social networking sites. This paper outlines the Graph theory and bounds. With the increase of users, there is a rapid as a tool for search operation on social networking sites & for increment in number of objects on social graph, results in optimization of search graph operation map reducing exponential growth of the graph size [12]. Accessing useful technology is discussed. In this paper on one side, different information from exponentially growing graph in fast traditional system of social networking and graph algorithm are elaborated. On the other side, various application and manner is a very difficult challenging task. To provide different issues related to the graph algorithm and social solution for the aforesaid problem it is the need to optimize networking are discussed in detail. -
Unicorn: a System for Searching the Social Graph
Unicorn: A System for Searching the Social Graph Michael Curtiss, Iain Becker, Tudor Bosman, Sergey Doroshenko, Lucian Grijincu, Tom Jackson, Sandhya Kunnatur, Soren Lassen, Philip Pronin, Sriram Sankar, Guanghao Shen, Gintaras Woss, Chao Yang, Ning Zhang Facebook, Inc. ABSTRACT rative of the evolution of Unicorn's architecture, as well as Unicorn is an online, in-memory social graph-aware index- documentation for the major features and components of ing system designed to search trillions of edges between tens the system. of billions of users and entities on thousands of commodity To the best of our knowledge, no other online graph re- servers. Unicorn is based on standard concepts in informa- trieval system has ever been built with the scale of Unicorn tion retrieval, but it includes features to promote results in terms of both data volume and query volume. The sys- with good social proximity. It also supports queries that re- tem serves tens of billions of nodes and trillions of edges quire multiple round-trips to leaves in order to retrieve ob- at scale while accounting for per-edge privacy, and it must jects that are more than one edge away from source nodes. also support realtime updates for all edges and nodes while Unicorn is designed to answer billions of queries per day at serving billions of daily queries at low latencies. latencies in the hundreds of milliseconds, and it serves as an This paper includes three main contributions: infrastructural building block for Facebook's Graph Search • We describe how we applied common information re- product. In this paper, we describe the data model and trieval architectural concepts to the domain of the so- query language supported by Unicorn. -
Graph Generators: State of the Art and Open Challenges 0:3 Across All the Categories of Graph Generators That We Consider
0 Graph Generators: State of the Art and Open Challenges ANGELA BONIFATI, Lyon 1 University, France IRENA HOLUBOVA´ , Charles University, Prague, Czech Republic ARNAU PRAT-PEREZ´ , Sparsity-Technologies, Barcelona, Spain SHERIF SAKR, University of Tartu, Estonia The abundance of interconnected data has fueled the design and implementation of graph generators repro- ducing real-world linking properties, or gauging the effectiveness of graph algorithms, techniques and appli- cations manipulating these data. We consider graph generation across multiple subfields, such as Semantic Web, graph databases, social networks, and community detection, along with general graphs. Despite the disparate requirements of modern graph generators throughout these communities, we analyze them under a common umbrella, reaching out the functionalities, the practical usage, and their supported operations. We argue that this classification is serving the need of providing scientists, researchers and practitioners with the right data generator at hand for their work. This survey provides a comprehensive overview of the state-of-the-art graph generators by focusing on those that are pertinent and suitable for several data-intensive tasks. Finally, we discuss open challenges and missing requirements of current graph generators along with their future extensions to new emerging fields. 1. INTRODUCTION Graphs are ubiquitous data structures that span a wide array of scientific disciplines and are a subject of study in several subfields of computer science. Nowadays, due to the dawn of numerous tools and applications manipulating graphs, they are adopted as a rich data model in many data-intensive use cases involving disparate domain knowl- edge, mathematical foundations and computer science. Interconnected data is often- times used to encode domain-specific use cases, such as recommendation networks, social networks, protein-to-protein interactions, geolocation networks, and fraud de- tection analysis networks, to name a few. -
Graph Pattern Matching Revised for Social Network Analysis
Graph Pattern Matching Revised for Social Network Analysis Wenfei Fan University of Edinburgh & Harbin Institute of Technology [email protected] Abstract subgraphs G of G to which Q is isomorphic, i.e., there exists a bijective function h from the nodes of Q to the Graph pattern matching is fundamental to social network nodes of G such that (u, u )isanedgeinQ if and analysis. Traditional techniques are subgraph isomorphism only if (h(u),h(u )) is an edge in G ;or and graph simulation. However, these notions often impose • M Q, G too strong a topological constraint on graphs to find mean- graph simulation [42]: ( ) is a binary relation S ⊆ V × V V V ingful matches. Worse still, graphs in the real world are Q ,where Q and are the set of nodes in Q G typically large, with millions of nodes and billions of edges. and , respectively, such that It is often prohibitively expensive to compute matches – for each node u in VQ,thereexistsanodev in V in such graphs. With these comes the need for revising such that (u, v) ∈ S,and the notions of graph pattern matching and for developing – for each (u, v) ∈ S and each edge (u, u )inQ, techniques of querying large graphs, to effectively and there is an edge (v, v )inG such that (u ,v ) ∈ S. efficiently identify social communities or groups. This paper aims to provide an overview of recent advances Graph pattern matching has been extensively studied for in the study of graph pattern matching in social networks. pattern recognition, knowledge discovery, biology, chemin- (1) We present several revisions of the traditional notions formatics, dynamic network traffic and intelligence analysis, of graph pattern matching to find sensible matches in so- based on subgraph isomorphism (see [2, 16, 28, 57] for sur- cial networks. -
Arxiv:1403.5206V2 [Cs.SI] 30 Jul 2014
What is Tumblr: A Statistical Overview and Comparison Yi Chang‡, Lei Tang§, Yoshiyuki Inagaki† and Yan Liu‡ † Yahoo Labs, Sunnyvale, CA 94089, USA § @WalmartLabs, San Bruno, CA 94066, USA ‡ University of Southern California, Los Angeles, CA 90089 [email protected],[email protected], [email protected],[email protected] Abstract Traditional blogging sites, such as Blogspot6 and Living- Social7, have high quality content but little social interac- Tumblr, as one of the most popular microblogging platforms, tions. Nardi et al. (Nardi et al. 2004) investigated blogging has gained momentum recently. It is reported to have 166.4 as a form of personal communication and expression, and millions of users and 73.4 billions of posts by January 2014. showed that the vast majority of blog posts are written by While many articles about Tumblr have been published in ordinarypeople with a small audience. On the contrary, pop- major press, there is not much scholar work so far. In this pa- 8 per, we provide some pioneer analysis on Tumblr from a va- ular social networking sites like Facebook , have richer so- riety of aspects. We study the social network structure among cial interactions, but lower quality content comparing with Tumblr users, analyze its user generated content, and describe blogosphere. Since most social interactions are either un- reblogging patterns to analyze its user behavior. We aim to published or less meaningful for the majority of public audi- provide a comprehensive statistical overview of Tumblr and ence, it is natural for Facebook users to form different com- compare it with other popular social services, including blo- munities or social circles. -
The Anatomy of Large Facebook Cascades
The Anatomy of Large Facebook Cascades P. Alex Dow Lada A. Adamic Adrien Friggeri Facebook Facebook Facebook [email protected] [email protected] [email protected] Abstract 2012; Bhattacharya and Ram 2012). Counterexamples in- clude email chain letters which have been measured to When users post photos on Facebook, they have the op- tion of allowing their friends, followers, or anyone at reach depths of hundreds of steps (Liben-Nowell and Klein- all to subsequently reshare the photo. A portion of the berg 2008), although they potentially also have substantial billions of photos posted to Facebook generates cas- breadth (Golub and Jackson 2010). A specific instance of a cades of reshares, enabling many additional users to copy-and-paste meme on Facebook similarly reached a max- see, like, comment, and reshare the photos. In this pa- imum depth of 40 (Adamic, Lento, and Fiore 2012). per we present characteristics of such cascades in ag- There are therefore what appear to be conflicting find- gregate, finding that a small fraction of photos account ings. On the one hand, large-scale aggregate analyses of con- for a significant proportion of reshare activity and gen- tent that is propagating in online media shows little or no erate cascades of non-trivial size and depth. We also show that the true influence chains in such cascades can evidence of viral spread. On the other there are instances be much deeper than what is visible through direct at- where careful tracing of cascades through email headers, tribution. To illuminate how large cascades can form, content structure, and a known contact graph, has yielded we study the diffusion trees of two widely distributed cascades that have achieved a non-trivial depth. -
Growing Your Facebook Group
Growing Your Facebook Group Moderator:Alaina Capasso [email protected] RI Small Business Development Center Webinar Coordinator Presentation by: Amanda Basse We exist to train, educate, and support entrepreneurs of both new (pre-venture) and established small businesses. Positioned within the nationwide network of SBDCs, we offer resources, key connections at the state and national level, workshops, and online and in-person support that equips us to help Ocean State entrepreneurs reach the next level of growth. Picture This Imagine having a group with hundreds of active members – interested specifically in what your business solutions provide. Imagine having a group with hundreds of active members - looking to you as the authority in your industry. What is a Facebook Group Facebook Groups are free communities within Facebook’s platform that users can join and participation is encouraged. Groups are built around common interests or goals, like pet training or gardening, but there are plenty of business-focused groups too. Facebook Groups are not new. In fact, they’ve been around for a decade, making them one of the social network’s oldest features. But after the Facebook algorithm update that de-prioritizes brand content, these groups have become even more important for connecting with a brand's followers. More than half of the network’s users (1.4 billion) are members of Facebook Groups. One in seven users ( about 400 million) belongs to what Facebook calls “meaningful Groups” – those that play a key role in their members’ daily lives. These are groups spend extensive time in per day, and feel part of a community within these groups.