Framework for Social Media Analysis Based on Hashtag Research
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Looking for Podcast Suggestions? We’Ve Got You Covered
Looking for podcast suggestions? We’ve got you covered. We asked Loomis faculty members to share their podcast playlists with us, and they offered a variety of suggestions as wide-ranging as their areas of personal interest and professional expertise. Here’s a collection of 85 of these free, downloadable audio shows for you to try, listed alphabetically with their “recommenders” listed below each entry: 30 for 30 You may be familiar with ESPN’s 30 for 30 series of award-winning sports documentaries on television. The podcasts of the same name are audio documentaries on similarly compelling subjects. Recent podcasts have looked at the man behind the Bikram Yoga fitness craze, racial activism by professional athletes, the origins of the hugely profitable Ultimate Fighting Championship, and the lasting legacy of the John Madden Football video game. Recommended by Elliott: “I love how it involves the culture of sports. You get an inner look on a sports story or event that you never really knew about. Brings real life and sports together in a fantastic way.” 99% Invisible From the podcast website: “Ever wonder how inflatable men came to be regular fixtures at used car lots? Curious about the origin of the fortune cookie? Want to know why Sigmund Freud opted for a couch over an armchair? 99% Invisible is about all the thought that goes into the things we don’t think about — the unnoticed architecture and design that shape our world.” Recommended by Scott ABCA Calls from the Clubhouse Interviews with coaches in the American Baseball Coaches Association Recommended by Donnie, who is head coach of varsity baseball and says the podcast covers “all aspects of baseball, culture, techniques, practices, strategy, etc. -
A Study on Social Network Analysis Through Data Mining Techniques – a Detailed Survey
ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 IJARCCE International Journal of Advanced Research in Computer and Communication Engineering ICRITCSA M S Ramaiah Institute of Technology, Bangalore Vol. 5, Special Issue 2, October 2016 A Study on Social Network Analysis through Data Mining Techniques – A detailed Survey Annie Syrien1, M. Hanumanthappa2 Research Scholar, Department of Computer Science and Applications, Bangalore University, India1 Professor, Department of Computer Science and Applications, Bangalore University, India2 Abstract: The paper aims to have a detailed study on data collection, data preprocessing and various methods used in developing a useful algorithms or methodologies on social network analysis in social media. The recent trends and advancements in the big data have led many researchers to focus on social media. Web enabled devices is an another important reason for this advancement, electronic media such as tablets, mobile phones, desktops, laptops and notepads enable the users to actively participate in different social networking systems. Many research has also significantly shows the advantages and challenges that social media has posed to the research world. The principal objective of this paper is to provide an overview of social media research carried out in recent years. Keywords: data mining, social media, big data. I. INTRODUCTION Data mining is an important technique in social media in scrutiny and analysis. In any industry data mining recent years as it is used to help in extraction of lot of technique based reports become the base line for making useful information, whereas this information turns to be an decisions and formulating the decisions. This report important asset to both academia and industries. -
Social Media Analytics
MEDIA DEVELOPMENT Social media analytics A practical guidebook for journalists and other media professionals Imprint PUBLISHER EDITORS Deutsche Welle Dr. Dennis Reineck 53110 Bonn Anne-Sophie Suntrop Germany SCREENSHOTS RESPONSIBLE Timo Lüge Carsten von Nahmen Helge Schroers Petra Berner PUBLISHED AUTHOR June 2019 Timo Lüge © DW Akademie MEDIA DEVELOPMENT Social media analytics A practical guidebook for journalists and other media professionals INTRODUCTION Introduction Having a successful online presence is becoming more and In part 2, we will look at some of the basics of social media more important for media outlets all around the world. In analysis. We’ll explore what different social media metrics 2018, 435 million people in Africa had access to the Internet mean and which are the most important. and 191 million of them were using social media.1 Today, Africa is one of the fastest growing regions for Internet access and Part 3 looks briefly at the resources you should have in place social media use. to effectively analyze your online communication. For journalists, this means new and exciting opportunities to Part 4 is the main part of the guide. In this section, we are connect with their › audiences. Passive readers, viewers and looking at Facebook, Twitter, YouTube and WhatsApp and will listeners are increasingly becoming active participants in a show you how to use free analytics tools to find out more dialogue that includes journalists and other community mem- about your communication and your audience. Instagram is bers. At the same time, social media is consuming people’s not covered in this guide because, at the time of writing, only attention: Time that used to be spent listening to the radio very few DW Akademie partners in Africa were active on the is now spent scrolling through Facebook, Twitter, Instagram, platform. -
How to Find the Best Hashtags for Your Business Hashtags Are a Simple Way to Boost Your Traffic and Target Specific Online Communities
CHECKLIST How to find the best hashtags for your business Hashtags are a simple way to boost your traffic and target specific online communities. This checklist will show you everything you need to know— from the best research tools to tactics for each social media network. What is a hashtag? A hashtag is keyword or phrase (without spaces) that contains the # symbol. Marketers tend to use hashtags to either join a conversation around a particular topic (such as #veganhealthchat) or create a branded community (such as Herschel’s #WellTravelled). HOW TO FIND THE BEST HASHTAGS FOR YOUR BUSINESS 1 WAYS TO USE 3 HASHTAGS 1. Find a specific audience Need to reach lawyers interested in tech? Or music lovers chatting about their favorite stereo gear? Hashtags are a simple way to find and reach niche audiences. 2. Ride a trend From discovering soon-to-be viral videos to inspiring social movements, hashtags can quickly connect your brand to new customers. Use hashtags to discover trending cultural moments. 3. Track results It’s easy to monitor hashtags across multiple social channels. From live events to new brand campaigns, hashtags both boost engagement and simplify your reporting. HOW TO FIND THE BEST HASHTAGS FOR YOUR BUSINESS 2 HOW HASHTAGS WORK ON EACH SOCIAL NETWORK Twitter Hashtags are an essential way to categorize content on Twitter. Users will often follow and discover new brands via hashtags. Try to limit to two or three. Instagram Hashtags are used to build communities and help users find topics they care about. For example, the popular NYC designer Jessica Walsh hosts a weekly Q&A session tagged #jessicasamamondays. -
What Is Gab? a Bastion of Free Speech Or an Alt-Right Echo Chamber?
What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber? Savvas Zannettou Barry Bradlyn Emiliano De Cristofaro Cyprus University of Technology Princeton Center for Theoretical Science University College London [email protected] [email protected] [email protected] Haewoon Kwak Michael Sirivianos Gianluca Stringhini Qatar Computing Research Institute Cyprus University of Technology University College London & Hamad Bin Khalifa University [email protected] [email protected] [email protected] Jeremy Blackburn University of Alabama at Birmingham [email protected] ABSTRACT ACM Reference Format: Over the past few years, a number of new “fringe” communities, Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Haewoon Kwak, like 4chan or certain subreddits, have gained traction on the Web Michael Sirivianos, Gianluca Stringhini, and Jeremy Blackburn. 2018. What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber?. In WWW at a rapid pace. However, more often than not, little is known about ’18 Companion: The 2018 Web Conference Companion, April 23–27, 2018, Lyon, how they evolve or what kind of activities they attract, despite France. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3184558. recent research has shown that they influence how false informa- 3191531 tion reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web’s information ecosystem. 1 INTRODUCTION In August 2016, a new social network called Gab was created The Web’s information ecosystem is composed of multiple com- as an alternative to Twitter. -
CDC Social Media Guidelines: Facebook Requirements and Best Practices
Social Media Guidelines and Best Practices Facebook Purpose This document is designed to provide guidance to Centers for Disease Control and Prevention employees and contractors on the process for planning and development, as well as best practices for participating and engaging, on the social networking site Facebook. Background Facebook is a social networking service launched in February 2004. As of March 2012, Facebook has more than 901 million active users, who generate an average of 3.2 billion Likes and Comments per day. For additional information on Facebook, visit http://newsroom.fb.com/. The first CDC Facebook page, managed by the Office of the Associate Director for Communication Science (OADC), Division of News and Electronic Media (DNEM), Electronic Media Branch (EMB), was launched in May 2009 to share featured health and safety updates and to build an active and participatory community around the work of the agency. The agency has expanded its Facebook presence beyond the main CDC profile, and now supports multiple Facebook profiles connecting users with information on a range of CDC health and safety topics. Communications Strategy Facebook, as with other social media tools, is intended to be part of a larger integrated health communications strategy or campaign developed under the leadership of the Associate Director of Communication Science (ADCS) in the Health Communication Science Office (HCSO) of CDC’s National Centers, Institutes, and Offices (CIOs). Clearance and Approval 1. New Accounts: As per the CDC Enterprise Social Media policy (link not available outside CDC network): • All new Facebook accounts must be cleared by the program’s HCSO office. -
Arxiv:1809.06223V1 [Cs.CL] 17 Sep 2018
Unsupervised Sense-Aware Hypernymy Extraction Dmitry Ustalov†, Alexander Panchenko‡, Chris Biemann‡, and Simone Paolo Ponzetto† †University of Mannheim, Germany fdmitry,[email protected] ‡University of Hamburg, Germany fpanchenko,[email protected] Abstract from text between two ambiguous words, e.g., apple fruit. However, by definition in Cruse In this paper, we show how unsupervised (1986), hypernymy is a binary relationship between sense representations can be used to im- senses, e.g., apple2 fruit1, where apple2 is the prove hypernymy extraction. We present “food” sense of the word “apple”. In turn, the word a method for extracting disambiguated hy- “apple” can be represented by multiple lexical units, pernymy relationships that propagate hy- e.g., “apple” or “pomiculture”. This sense is dis- pernyms to sets of synonyms (synsets), tinct from the “company” sense of the word “ap- constructs embeddings for these sets, and ple”, which can be denoted as apple3. Thus, more establishes sense-aware relationships be- generally, hypernymy is a relation defined on two tween matching synsets. Evaluation on two sets of disambiguated words; this modeling princi- gold standard datasets for English and Rus- ple was also implemented in WordNet (Fellbaum, sian shows that the method successfully 1998), where hypernymy relations link not words recognizes hypernymy relationships that directly, but instead synsets. This essential prop- cannot be found with standard Hearst pat- erty of hypernymy is however not used or modeled terns and Wiktionary datasets for the re- in the majority of current hypernymy extraction spective languages. approaches. In this paper, we present an approach that addresses this shortcoming. -
Mining Social Networking Sites: a Specific Area of Facebook
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019 Mining Social Networking Sites: A Specific area of Facebook Sonam, Surjeet Kumar Abstract: Now a days facebook is the specific social network site for communicating more people, to develop learning process II. BACKGROUND AND RELATED WORK & research area. Although mining and analysis are needed area of social network so, we are showing useful part in this paper We studied several literatures and got techniques that are that are related to communication with people, collection of data useful for collecting and analyze specific information. We and uses of social network’s tool and techniques .During our are discussing some techniques such as: research work we found several tools are available for collecting A. OSMNs Dataset: Mining related data from the Web data and analyzing fan pages. For the purpose of our research mining platform by using tools such as OSMN's Web work, we used social network site such as Facebook, in this platform we created one page related to blog who did the panacea extraction technology. Since the OSMN dataset resides in for us. In this research paper we have applied online social media the backend server, it is not available in the public domain network for extracting data. Uniform node detection has used for and can therefore only be accessed through the web finding common properties among audience as well as Regular interface. FB uses many access algorithms, such as Random equivalence nodes are using for calculating uniformity. Effective Walk or BFS [25]. -
How to Prove Your Social Media Works
HOW TO PROVE YOUR SOCIAL MEDIA WORKS Ariana Torres, PhD Marketing Specialist Purdue University Agenda § Is your business getting anything out of its social media engagement? § Some key words in social media ROI § Measuring your social media ROI 1. Define why you want to do social media for your business 2. Set measurable goals 3. Track goals 4. Track social media expenses (per campaign) 5. Calculate campaign-specific ROI § How to improve your ROI § An argument for using both organic and paid social media Is your business getting anything out of its social media engagement? § Measuring return on investment (ROI) is the number one challenge of social marketers § What does ROI really mean? It is brand awareness, number of followers, engagement rate, customer satisfaction, lead to conversion, or… § Not everything you do in social media has an immediate effect into $ § Yet, it is important to track impact of time and resources that go into your social media efforts § Your social media goals will determine what you will measure à metrics • Every campaign needs a goal, and every goal needs a metric § Metrics will prove if your campaign is being successful, if you have to change or pivot something § Each social media platform has its analytics • Facebook à Insights • Twitter à Twitter analytics • Instagram à Insights Some key words § Engagement: likes, shares, followers, comments § Reach: how many people have viewed your content § Impressions: how many times your posts show up in a feeds/timeline and may connect to your audience § Conversions: lead conversion rate is how many people “buy” something § Social proofing: people copy others behavior, i.e. -
Roberto Navigli Curriculum Vitæ
Roberto Navigli Curriculum Vitæ Roma, 18 marzo 2021 Part I { General Information Full Name Roberto Navigli Citizenship Italian E-mail [email protected] Spoken languages Italian, English, French Sito Web http://wwwusers.di.uniroma1.it/∼navigli Part II { Education Type Year Institution Notes (Degree, Experience, etc.) Ph.D. 2007 Sapienza PhD in Computer Science, advisor: prof. Paola Velardi. Thesis: "Structural Semantic Interconnections: a Knowledge-Based WSD Algorithm, its Evaluation and Applications", Winner of the 2017 AI*IA \Marco Cadoli" national prize for the best PhD thesis in AI. University 2001 Sapienza Master degree in Computer Science, 110/110 cum laude. graduation Thesis: "An automatic algorithm for domain ontology learning", supervisor: prof. Paola Velardi. Part III { Appointments III(A) { Academic Appointments Start End Institution Position 09/2017 oggi Sapienza full professor, Dipartimento di Informatica, Universit`adi Roma \La Sapienza". 12/2010 08/2017 Sapienza associate professor, Dipartimento di Informatica, Universit`adi Roma \La Sapienza". 03/2007 12/2010 Sapienza assistant professor, Dipartimento di Informatica, Sapienza. 05/2003 03/2007 Sapienza research fellow, Dipartimento di Informatica, Sapienza. III(B) { Other Appointments Start End Institution Position 05/2000 04/2003 YH Reply software engineer S.p.A. Roma 1 III(C) { Research visits and stays Start End Institution Position 01/2015 01/2015 Center for Advanced Studies Invited Visiting Fellow (CAS), LMU, Germany 2010 2012 University of Wolverhampton Visiting -
Social Media Monitoring During Elections
SOCIAL MEDIA MONITORING DURING ELECTIONS CASES AND BEST PRACTICE TO INFORM ELECTORAL OBSERVATION MISSIONS AUTHORS Rafael Schmuziger Goldzweig Bruno Lupion Michael Meyer-Resende FOREWORD BY Iskra Kirova and Susan Morgan EDITOR Ros Taylor © 2019 Open Society Foundations uic b n dog. This publication is available as a PDF on the Open Society Foundations website under a Creative Commons license that allows copying and distributing the publication, only in its entirety, as long as it is attributed to the Open Society Foundations and used for noncommercial educational or public policy purposes. Photographs may not be used separately from the publication. Cover photo: © Geert Vanden Wijngaert/Bloomberg/Getty opensocietyfoundations.org Experiences of Social Media Monitoring During Elections: Cases and Best Practice to Inform Electoral Observation Missions May 2019 CONTENTS 3 FOREWORD 5 EXECUTIVE SUMMARY 7 INTRODUCTION 9 CONTEXT 9 Threats to electoral integrity in social media 9 The framework of international election observation 10 The Declaration of Principles of International Election Observation 12 WHAT ARE INTERNATIONAL OBSERVER MISSIONS DOING ON SOCIAL MEDIA DISCOURSE? 14 WHAT ARE EUROPEAN GOVERNMENTS DOING? 17 WHAT ARE CIVIL SOCIETY ORGANIZATIONS DOING? 17 European civil society 19 A glimpse beyond Europe 21 Non-government initiatives: Europe & beyond 23 Topics/objects of analysis 24 Platforms 26 Monitoring tools 1 Experiences of Social Media Monitoring During Elections: Cases and Best Practice to Inform Electoral Observation Missions May 2019 27 GUIDELINES FOR EOMS 27 Monitoring of social media vs traditional media 29 Cooperation between actors and information exchange partnerships 29 Dealing with data 29 Working in different contexts 31 SWOT analysis for EOMs in social media monitoring 32 RECOMMENDATIONS 35 ANNEX 1: SURVEY OF INTERNATIONAL ELECTION OBSERVATION ORGANIZATIONS 36 ANNEX 2: BRIEF DESCRIPTION OF THE INITIATIVES ANALYSED (based on interviews) 36 a. -
Ontology Learning and Its Application to Automated Terminology Translation
Natural Language Processing Ontology Learning and Its Application to Automated Terminology Translation Roberto Navigli and Paola Velardi, Università di Roma La Sapienza Aldo Gangemi, Institute of Cognitive Sciences and Technology lthough the IT community widely acknowledges the usefulness of domain ontolo- A gies, especially in relation to the Semantic Web,1,2 we must overcome several bar- The OntoLearn system riers before they become practical and useful tools. Thus far, only a few specific research for automated ontology environments have ontologies. (The “What Is an Ontology?” sidebar on page 24 provides learning extracts a definition and some background.) Many in the and is part of a more general ontology engineering computational-linguistics research community use architecture.4,5 Here, we describe the system and an relevant domain terms WordNet,3 but large-scale IT applications based on experiment in which we used a machine-learned it require heavy customization. tourism ontology to automatically translate multi- from a corpus of text, Thus, a critical issue is ontology construction— word terms from English to Italian. The method can identifying, defining, and entering concept defini- apply to other domains without manual adaptation. relates them to tions. In large, complex application domains, this task can be lengthy, costly, and controversial, because peo- OntoLearn architecture appropriate concepts in ple can have different points of view about the same Figure 1 shows the elements of the architecture. concept. Two main approaches aid large-scale ontol- Using the Ariosto language processor,6 OntoLearn a general-purpose ogy construction. The first one facilitates manual extracts terminology from a corpus of domain text, ontology engineering by providing natural language such as specialized Web sites and warehouses or doc- ontology, and detects processing tools, including editors, consistency uments exchanged among members of a virtual com- checkers, mediators to support shared decisions, and munity.