This technical report presents Ediscope, a system for analyzing social engagement with online news articles. Ediscope collects social data like likes, shares, and clicks from Facebook, Twitter, and Bitly about news articles. The report finds that less than 20% of social activity around a news article happens after 24 hours of publication. On average, a news article gets 5-20 social interactions per 1,000 pageviews. There is a low correlation between social shares and actual pageviews, especially for non-top news articles. Based on these findings, the report suggests ways online news organizations can optimize social sharing and content strategy.
Google is the dominant search engine, crawling and indexing webpages to understand their content and how they relate to each other. It then ranks pages based on over 200 factors, with the goal of displaying the most relevant results first. Search engine optimization (SEO) aims to help websites rank higher through both on-site techniques like optimizing content and design, and off-site efforts like building links and social media presence. Understanding how users behave online through search queries and on-site behavior is important for SEO success. The document provides an overview of how Google works and recommendations for an SEO best practices guideline.
Jan 11 2013 learning lab 2013 show me the metricsHack the Hood
The document discusses best practices for using metrics to evaluate and grow a website or digital project. It provides an overview of key metrics tools like Google Analytics, Facebook Insights, and Tweetreach and the types of data they can provide about traffic, audience demographics, engagement, and content performance. It also offers tips on how to analyze metrics to make decisions about content strategy, social media marketing, and site improvements.
March 6 building visibility for yr projectHack the Hood
Want to get more eyeballs on your project or site? Use the 5 best practices in SEO, social media marketing, using metrics and partnering to grow audience--with maximum impact, more minimal effort.
Facebook's impact on ideological segregationGeorge Farra
The document discusses Facebook's EdgeRank algorithm which determines what content is shown on a user's News Feed. It factors in elements like affinity (how often a user interacts with certain friends/pages), weight (more effort interactions like comments are valued higher), and time (fresher content is prioritized). This personalized ranking system has faced criticism for potentially creating filter bubbles and spreading misinformation. While designed to keep users engaged, it also incentivizes certain rule-following behaviors to increase visibility on the News Feed.
Silverpop Engage S2 S Study Emails Go ViralMarketingfacts
This document summarizes the key findings of a study conducted by Silverpop on measuring "share to social" performance of emails. Some of the main findings include:
1) Most companies include links to 4-5 social networks in their emails, with Facebook, MySpace, and Twitter being the most common.
2) On average, emails generate clicks on social sharing links for 6.8 days.
3) Only 35% of emails studied generated any social sharing clicks, showing simply including sharing links is not enough.
4) Facebook dominates social sharing, outperforming other networks on most metrics measured.
Google Updates: Panda, Penguin and Hummingbird, Oh My!MITCPS
According to Compete PRO, 34.52% of the incoming traffic to mit.edu sites in July 2014 came from Google.com. That’s 1,403,774 out of the 4,065,881 visits to these sites from the U.S. that month. This presentation will explain how search works and give an overview of the more than 200 unique signals or “clues” that Google’s algorithms use today. It will also cover three major updates to Google’s algorithms, named Panda, Penguin and Hummingbird. And it will take a look at the algorithms of the second largest search engine, YouTube.
This document is a summary of a social media benchmarks report that analyzes the posting, following, and engagement behaviors of over 7,000 businesses across different industries and company sizes. Some of the key findings include: 1) There is no correlation between number of posts and engagement levels; 2) Size of a company's following is a better predictor of engagement than post frequency; and 3) Engagement is driven by balancing factors like following size, post frequency, and post quality rather than any single metric. The report provides detailed data on these metrics broken down by industry and size.
This document discusses using social media during emergencies. It provides statistics on social media usage and explains why businesses should use social media. During emergencies, social media can be used to provide information to customers and the public, monitor discussions, and spread important messages. The document outlines challenges of social media use and provides examples of how organizations have effectively utilized platforms like Twitter and Facebook during crises.
Google is the dominant search engine, crawling and indexing webpages to understand their content and how they relate to each other. It then ranks pages based on over 200 factors, with the goal of displaying the most relevant results first. Search engine optimization (SEO) aims to help websites rank higher through both on-site techniques like optimizing content and design, and off-site efforts like building links and social media presence. Understanding how users behave online through search queries and on-site behavior is important for SEO success. The document provides an overview of how Google works and recommendations for an SEO best practices guideline.
Jan 11 2013 learning lab 2013 show me the metricsHack the Hood
The document discusses best practices for using metrics to evaluate and grow a website or digital project. It provides an overview of key metrics tools like Google Analytics, Facebook Insights, and Tweetreach and the types of data they can provide about traffic, audience demographics, engagement, and content performance. It also offers tips on how to analyze metrics to make decisions about content strategy, social media marketing, and site improvements.
March 6 building visibility for yr projectHack the Hood
Want to get more eyeballs on your project or site? Use the 5 best practices in SEO, social media marketing, using metrics and partnering to grow audience--with maximum impact, more minimal effort.
Facebook's impact on ideological segregationGeorge Farra
The document discusses Facebook's EdgeRank algorithm which determines what content is shown on a user's News Feed. It factors in elements like affinity (how often a user interacts with certain friends/pages), weight (more effort interactions like comments are valued higher), and time (fresher content is prioritized). This personalized ranking system has faced criticism for potentially creating filter bubbles and spreading misinformation. While designed to keep users engaged, it also incentivizes certain rule-following behaviors to increase visibility on the News Feed.
Silverpop Engage S2 S Study Emails Go ViralMarketingfacts
This document summarizes the key findings of a study conducted by Silverpop on measuring "share to social" performance of emails. Some of the main findings include:
1) Most companies include links to 4-5 social networks in their emails, with Facebook, MySpace, and Twitter being the most common.
2) On average, emails generate clicks on social sharing links for 6.8 days.
3) Only 35% of emails studied generated any social sharing clicks, showing simply including sharing links is not enough.
4) Facebook dominates social sharing, outperforming other networks on most metrics measured.
Google Updates: Panda, Penguin and Hummingbird, Oh My!MITCPS
According to Compete PRO, 34.52% of the incoming traffic to mit.edu sites in July 2014 came from Google.com. That’s 1,403,774 out of the 4,065,881 visits to these sites from the U.S. that month. This presentation will explain how search works and give an overview of the more than 200 unique signals or “clues” that Google’s algorithms use today. It will also cover three major updates to Google’s algorithms, named Panda, Penguin and Hummingbird. And it will take a look at the algorithms of the second largest search engine, YouTube.
This document is a summary of a social media benchmarks report that analyzes the posting, following, and engagement behaviors of over 7,000 businesses across different industries and company sizes. Some of the key findings include: 1) There is no correlation between number of posts and engagement levels; 2) Size of a company's following is a better predictor of engagement than post frequency; and 3) Engagement is driven by balancing factors like following size, post frequency, and post quality rather than any single metric. The report provides detailed data on these metrics broken down by industry and size.
This document discusses using social media during emergencies. It provides statistics on social media usage and explains why businesses should use social media. During emergencies, social media can be used to provide information to customers and the public, monitor discussions, and spread important messages. The document outlines challenges of social media use and provides examples of how organizations have effectively utilized platforms like Twitter and Facebook during crises.
This document discusses how big data impacts social media. It begins with an overview of terminology related to big data, metadata, analytics and the four V's of big data. It then discusses how analytics can be used with social media and big data, providing tips and tools for developing a strategic plan. Specific social media platforms like Facebook, Twitter, Pinterest and Instagram are discussed. The document also addresses some concerns around big data like privacy and addresses how to mitigate these concerns through policies and community guidelines.
Presentation big data and social media final_videoramikaurraminder
The document discusses the challenges and opportunities of analyzing big data from social media. It notes that social media generates the largest record of human activity but making sense of the unstructured data is a challenge. It provides examples of how companies use social media data for applications like credit risk assessment and personalized recommendations. The document also discusses privacy and ethical issues with social media data mining, and best practices for social media marketers to leverage big data insights.
The document describes how a news reading app can provide personalized content recommendations by leveraging what it learns about a user's interests from their social media accounts and reading habits. It outlines how the app would monitor social networks, analyze content and user interactions to build models of individual users and communities. These models are then used to select and rank news stories that closely match a user's interests to create a personalized news experience tailored to each reader.
Investor Relations & Emerging Media – Presented at the NIRI Capital Area Chap...Michael Pranikoff
The document discusses best practices for using emerging media and social networks in investor relations and communications. It provides statistics on social media usage among financial professionals and investors. It recommends establishing guidelines for social media use, consistently communicating through multiple channels, listening to conversations, and addressing risks while experimenting with new technologies. The goal is to engage audiences and drive two-way conversations in an open and trusted manner.
Web 2.0 Measurement: Open Government Innovations ConferenceAndrew Krzmarzick
Presentation delivered at the Open Government and Innovations (OGI) Conference in Washington, DC, on July 22, 2009. Outlines the ways in which government has measured its web presence in a "1.0" context, including an overview of the measurement activities conducted by Brookings Institution, Foresee, Forrester and the e-Government Act of 2002.
The document summarizes research issues, tools, and applications related to analyzing the blogosphere. It discusses how social networks form online through blogs and user-generated content. Various approaches are presented for modeling, analyzing, and mining the blogosphere to study influence, communities, and other phenomena. Tools and methods mentioned include network analysis, text mining, and simulating the blogosphere as a complex social network with nodes and relationships.
The document summarizes findings from analyzing online discussions related to the 2010 UK election using text analysis software. Some key findings include:
1) Changes in the daily share of online discussion about political parties correlated with and could predict changes in polling results from the next day.
2) "Traditional media" websites like newspapers had much higher influence scores than social media, indicating they drove more of the online discussion.
3) The Lib Dems responded strongly to increases in attention online, like a new brand, while Labour and Conservatives seemed to try to limit discussion of the Lib Dems.
This document provides a high-level and low-level description of a sentiment analysis system. At the high level, it collects text data, splits it into sentences, assigns polarity, checks for repeated words, and extracts sentiment. The low-level description details how it collects data from Facebook using APIs, processes the data by tagging parts of speech, analyzes polarity vs neutral sets, lists features, and builds a classifier using naive Bayes and dependencies between n-grams and parts of speech. The system aims to analyze sentiment from social media texts at both the document and sentence level.
This document provides an analysis of the online social network industry through a comparison of Facebook and Tuenti. It begins with an executive summary that outlines the report will analyze the industry through an external analysis using PEST and Porter's Five Forces models, and an internal analysis of Facebook and Tuenti's value chains and resources.
The document then provides background information on Facebook, the largest global social network, and Tuenti, the largest social network in Spain. It compares their histories, users, revenues and strategies.
It conducts an external analysis of the industry through a PEST analysis that finds political pressures around privacy and security laws, economic opportunities in advertising, and social pressures of adoption. A technological factor is the creation of
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Uw Digital Communications Social Media Is Not SearchMarianne Sweeny
I had the pleasure of speaking to one of the Digital Communication classes at the University of Washington on my favorite topic, why social media will never replace search as an information finding medium. Those students were wicked smart and I walked away learning a lot myself.
Overview of why and how web2.0 matters for eGovernment. Presented at EU ministerial conference on eGovernment (download it at www.egov2007.gov.pt).
NEW REPORT on this available at www.jrc.es
The concept of Big Data emphasizes the use of the complete data set to analyze process and predict various phenomena in the business world. This document describes the business uses of Big Data and outlines a Strategy for implementing Big Data analytics for Social Media
Kaplan & Haenlein - Users of the world, unite - the challenges and opportunit...ESCP Exchange
The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term ‘‘Social Media’’ exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.
Tagging - Can User Generated Content Improve Our Services?guestff5a190a
This document discusses introducing user tagging to statistical websites to help users find information. Tagging allows users to add their own keywords to describe content, creating a "folksonomy" of user-defined terms rather than relying solely on predefined categories. Tagging is common on websites like Flickr and YouTube where users can tag photos and videos. The document analyzes the potential strengths and weaknesses of introducing tagging to statistical websites, noting it could help aggregation but may lead to unbalanced or incorrect initial tags.
Link exchanges were once an effective way to improve search engine rankings by gaining backlinks, but are now considered spam by search engines. Search engines no longer value reciprocal links and instead focus on relevant one-way links from other sites to determine a site's importance. Participating in link exchanges can now negatively impact a site's rankings and companies have even been denied inclusion in search engine directories for using them. The best strategies now are to get relevant backlinks through directory submissions, article publishing, and social bookmarking.
1) The document discusses how social media is impacting search engine optimization and how search engines are beginning to value social signals. It notes that links used to come from technical influencers but now come more from average users via social sharing.
2) It introduces the concept of a "share graph" that search engines are using to measure sharing activity across social networks and determine authority and trust.
3) It emphasizes that social media and search engine marketing can no longer be separate and that integration is key to success in getting found online.
This document discusses four key issues in Australian family law: 1) Same-sex relationships and marriage equality, noting that same-sex marriage is currently excluded but civil unions are recognized; 2) Changing parental responsibility which now emphasizes both parents rather than just mothers; 3) Surrogacy and birth technologies which raise legal questions around parental rights; and 4) Care and protection of children from domestic violence which several government acts have addressed but reports of violence continue to increase.
The document discusses yoga techniques for managing respiratory disorders like asthma. It explains how yoga aims to bridge the voluntary and involuntary nervous systems through breathing practices like pranayama and asanas. Specific techniques recommended include chair breathing, anuloma viloma, ujjayi, and bhramari pranayama as well as relaxation asanas to reduce stress and relax the body. Kriyas like neti are also suggested to clear nasal passages and manage allergies.
This document discusses how big data impacts social media. It begins with an overview of terminology related to big data, metadata, analytics and the four V's of big data. It then discusses how analytics can be used with social media and big data, providing tips and tools for developing a strategic plan. Specific social media platforms like Facebook, Twitter, Pinterest and Instagram are discussed. The document also addresses some concerns around big data like privacy and addresses how to mitigate these concerns through policies and community guidelines.
Presentation big data and social media final_videoramikaurraminder
The document discusses the challenges and opportunities of analyzing big data from social media. It notes that social media generates the largest record of human activity but making sense of the unstructured data is a challenge. It provides examples of how companies use social media data for applications like credit risk assessment and personalized recommendations. The document also discusses privacy and ethical issues with social media data mining, and best practices for social media marketers to leverage big data insights.
The document describes how a news reading app can provide personalized content recommendations by leveraging what it learns about a user's interests from their social media accounts and reading habits. It outlines how the app would monitor social networks, analyze content and user interactions to build models of individual users and communities. These models are then used to select and rank news stories that closely match a user's interests to create a personalized news experience tailored to each reader.
Investor Relations & Emerging Media – Presented at the NIRI Capital Area Chap...Michael Pranikoff
The document discusses best practices for using emerging media and social networks in investor relations and communications. It provides statistics on social media usage among financial professionals and investors. It recommends establishing guidelines for social media use, consistently communicating through multiple channels, listening to conversations, and addressing risks while experimenting with new technologies. The goal is to engage audiences and drive two-way conversations in an open and trusted manner.
Web 2.0 Measurement: Open Government Innovations ConferenceAndrew Krzmarzick
Presentation delivered at the Open Government and Innovations (OGI) Conference in Washington, DC, on July 22, 2009. Outlines the ways in which government has measured its web presence in a "1.0" context, including an overview of the measurement activities conducted by Brookings Institution, Foresee, Forrester and the e-Government Act of 2002.
The document summarizes research issues, tools, and applications related to analyzing the blogosphere. It discusses how social networks form online through blogs and user-generated content. Various approaches are presented for modeling, analyzing, and mining the blogosphere to study influence, communities, and other phenomena. Tools and methods mentioned include network analysis, text mining, and simulating the blogosphere as a complex social network with nodes and relationships.
The document summarizes findings from analyzing online discussions related to the 2010 UK election using text analysis software. Some key findings include:
1) Changes in the daily share of online discussion about political parties correlated with and could predict changes in polling results from the next day.
2) "Traditional media" websites like newspapers had much higher influence scores than social media, indicating they drove more of the online discussion.
3) The Lib Dems responded strongly to increases in attention online, like a new brand, while Labour and Conservatives seemed to try to limit discussion of the Lib Dems.
This document provides a high-level and low-level description of a sentiment analysis system. At the high level, it collects text data, splits it into sentences, assigns polarity, checks for repeated words, and extracts sentiment. The low-level description details how it collects data from Facebook using APIs, processes the data by tagging parts of speech, analyzes polarity vs neutral sets, lists features, and builds a classifier using naive Bayes and dependencies between n-grams and parts of speech. The system aims to analyze sentiment from social media texts at both the document and sentence level.
This document provides an analysis of the online social network industry through a comparison of Facebook and Tuenti. It begins with an executive summary that outlines the report will analyze the industry through an external analysis using PEST and Porter's Five Forces models, and an internal analysis of Facebook and Tuenti's value chains and resources.
The document then provides background information on Facebook, the largest global social network, and Tuenti, the largest social network in Spain. It compares their histories, users, revenues and strategies.
It conducts an external analysis of the industry through a PEST analysis that finds political pressures around privacy and security laws, economic opportunities in advertising, and social pressures of adoption. A technological factor is the creation of
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Uw Digital Communications Social Media Is Not SearchMarianne Sweeny
I had the pleasure of speaking to one of the Digital Communication classes at the University of Washington on my favorite topic, why social media will never replace search as an information finding medium. Those students were wicked smart and I walked away learning a lot myself.
Overview of why and how web2.0 matters for eGovernment. Presented at EU ministerial conference on eGovernment (download it at www.egov2007.gov.pt).
NEW REPORT on this available at www.jrc.es
The concept of Big Data emphasizes the use of the complete data set to analyze process and predict various phenomena in the business world. This document describes the business uses of Big Data and outlines a Strategy for implementing Big Data analytics for Social Media
Kaplan & Haenlein - Users of the world, unite - the challenges and opportunit...ESCP Exchange
The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term ‘‘Social Media’’ exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.
Tagging - Can User Generated Content Improve Our Services?guestff5a190a
This document discusses introducing user tagging to statistical websites to help users find information. Tagging allows users to add their own keywords to describe content, creating a "folksonomy" of user-defined terms rather than relying solely on predefined categories. Tagging is common on websites like Flickr and YouTube where users can tag photos and videos. The document analyzes the potential strengths and weaknesses of introducing tagging to statistical websites, noting it could help aggregation but may lead to unbalanced or incorrect initial tags.
Link exchanges were once an effective way to improve search engine rankings by gaining backlinks, but are now considered spam by search engines. Search engines no longer value reciprocal links and instead focus on relevant one-way links from other sites to determine a site's importance. Participating in link exchanges can now negatively impact a site's rankings and companies have even been denied inclusion in search engine directories for using them. The best strategies now are to get relevant backlinks through directory submissions, article publishing, and social bookmarking.
1) The document discusses how social media is impacting search engine optimization and how search engines are beginning to value social signals. It notes that links used to come from technical influencers but now come more from average users via social sharing.
2) It introduces the concept of a "share graph" that search engines are using to measure sharing activity across social networks and determine authority and trust.
3) It emphasizes that social media and search engine marketing can no longer be separate and that integration is key to success in getting found online.
This document discusses four key issues in Australian family law: 1) Same-sex relationships and marriage equality, noting that same-sex marriage is currently excluded but civil unions are recognized; 2) Changing parental responsibility which now emphasizes both parents rather than just mothers; 3) Surrogacy and birth technologies which raise legal questions around parental rights; and 4) Care and protection of children from domestic violence which several government acts have addressed but reports of violence continue to increase.
The document discusses yoga techniques for managing respiratory disorders like asthma. It explains how yoga aims to bridge the voluntary and involuntary nervous systems through breathing practices like pranayama and asanas. Specific techniques recommended include chair breathing, anuloma viloma, ujjayi, and bhramari pranayama as well as relaxation asanas to reduce stress and relax the body. Kriyas like neti are also suggested to clear nasal passages and manage allergies.
Rubrica para evaluación de recursos digitalesgloria bonilla
Este documento presenta una rubrica para evaluar recursos digitales educativos. La rubrica contiene cinco categorías de evaluación: 1) consideración pedagógica, 2) contenidos de aprendizaje, 3) características técnicas, 4) implementación y soporte, y 5) ética y derechos de autor. Cada categoría incluye elementos específicos que se califican en una escala de 0 a 2. La rubrica provee una puntuación total para cada categoría y una nota final para el recurso digital evaluado.
Laura Baker has over 7 years of experience as a water jet operator and programmer at Romeo Engineering where she was able to turn a two position job into a single position through her programming and operating skills. She is a certified forklift driver and safety manager who led daily safety meetings and trained new employees. Prior to Romeo Engineering, she worked as a bartender for 10 years where she managed the daytime bartender position and established a regular clientele. Baker brings experience in time management, programming, safety, and team skills to her next role.
This document discusses career development and exploration. It begins by defining self-exploration and assessment, which includes understanding one's interests, personality, skills, and values. It then discusses vocational assessments like the Myers-Briggs Type Indicator that provide insight. The document outlines ways to gain experience through internships, study abroad, research, and informational interviews. It concludes by encouraging making an appointment with the career services center to discuss career exploration options.
Raci casing spacers are widely used all over the world In water, oil and gas businesses in order to separate the carrier pipeline from the casing and the Raci casing spacers isolate water, sewer and gas pipelines from casing simply and effectively. They are also used in refineries where double containment pipe is requested for safety reasons.
Raci spacer on YouTube video:
http://bit.ly/1wJ8O0c
http://bit.ly/1Bqv51j
Managing and measuring social media coventry combinedWeb2LLP
Web2LLP Workshop, Coventry, 8 November 2013
Managing and Measuring the reach and impact of your social media activities
Auhtors: Gary Shochat (PAU Education) and Tatiana Codreanu (web2learn)
Metrics & Analytics helps companies measure and assess the performance of social media initiatives in relation to business objectives. Key metrics include conversations, brand mentions, engagement, memberships, activity ratios, and loyalty. Effective metrics & analytics require monitoring multiple social platforms and using various analytic tools to track metrics like sentiment, influence, competitors, and engagement over time. Currently no single tool can measure all aspects of social media, so most businesses use multiple tools to capture, analyze, and interpret social media data.
Data Science: 2018 Media & Influencer AnalysisZeno Group
The document analyzes media coverage and influencer activity related to data science over 2018. It finds that major publications like Forbes and TechCrunch drove the most volume of coverage, while topics like data analytics, data science, and predictive analytics resonated most. Key influencers are identified along with their social reach, areas of expertise, and trending topics discussed over time. Their top consumed media provides insights into where to engage them.
socialflow data drives social performance wpMohamed Mahdy
Organic social posting can still reach large audiences and generate engagement, but the vast majority of posts have modest performance compared to a small number of highly engaging "blockbuster" posts. A data-driven approach to social posting, where algorithms determine timing, can help companies improve reach by 91% and engagement by 25% over scheduled posts by allowing them to publish more tailored content. Media and entertainment companies tend to generate the most highly engaging posts due to their large amount of shareable content.
This document discusses metrics and analytics for social media. It defines metrics as counting and tracking past data, while analytics look at both past and present data strategically. Examples are provided of metrics like employee attrition rates versus analytics that examine the reasons behind those rates. Key frameworks for social media measurement are outlined, like return on investment, reach, likes, and cost of ignoring opportunities. Popular social media analytics tools from Google, Facebook, and Twitter are profiled. The document also discusses network analytics and using tools like NodeXL to map relationships and influence within social networks.
This document discusses metrics and analytics for social media. It defines metrics as counting and tracking past data, while analytics look at both past and present data strategically. Examples are provided of metrics like employee attrition rates versus analytics that examine the reasons behind those rates. Key frameworks for social media measurement are outlined, including types of engagement data from platforms like Facebook, Twitter, and Google Analytics. The document also covers principles of social network analysis and tools for mapping connections between users.
This document discusses metrics and analytics for social media. It defines metrics as counting and tracking past data, while analytics look at both past and present data strategically. Examples are provided of metrics like employee attrition rates versus analytics that examine the reasons behind those rates. Key frameworks for social media measurement are outlined, including types of engagement data from platforms like Facebook, Twitter, and Google Analytics. The document also covers principles of social network analysis for mapping influence through connections on platforms.
1. The document discusses creating an ICT project concept paper to propose an ICT project for social change. It provides guidelines for the key elements to include in a concept paper such as introduction, purpose, description, budget, and contact information.
2. It also provides a sample concept paper proposing a project to improve school drinking fountains. The concept paper introduces the issue, outlines the proposed online project using websites and petitions, and notes the short timeline and minimal budget needed.
3. The document then reviews the process for planning, developing, promoting, and maintaining an ICT project for social change and monitoring its impact. It also provides information on using tools like Google Forms to gather user feedback to evaluate projects
Social Media Report UK Political Parties 2014SocialWin
We have analysed the Twitter activity of the most important UK political parties: The Labour Party, The Conservatives, Liberal Democrats, UKIP, British National Party and The Green Party.
Mc graw hill social media analytics - case studies - tools - tactics - mars...Marshall Sponder
1. Social media analytics is transforming how communications are evaluated by making data and insights the new message.
2. Case studies show how analytics can increase traffic, engagement, and revenue for publishers by optimizing content distribution and recommendation.
3. Tools like Infinigraph, LinkedIn, and VisualRevenue use social media data and predictive algorithms to automate editorial decisions and curate content optimally.
Social Media Dashboarding by Scott Wilder and semphonicEdelman Digital
This document discusses social media dash boarding and measurement. It introduces Gary Angel and Scott K. Wilder as experts in social media analytics. It outlines challenges in measuring social media, including culling relevant data, classifying data by topic and sentiment, and providing business context. Examples of social media dashboards are provided to illustrate tracking metrics, competitors, influencers, and site performance. The key takeaways are applying the three C's of culling data, classifying data, and providing business context.
This document discusses social media dash boarding and measurement. It introduces Gary Angel and Scott K. Wilder as experts in social media analytics. It outlines challenges in measuring social media data and proposes focusing on the three C's: culling relevant data, classifying data by topic, sentiment, source and impact, and providing context by linking metrics to business issues. Examples of social media dashboards are provided to illustrate visualization techniques for competitive analysis, tracking influencers, and measuring site performance and marketing efforts.
Achieving and measuring success on the social webBridey Lipscombe
The document discusses success on the social web and how to measure it. It outlines that success is built through developing relationships over time through many lightweight interactions. It also stresses defining objectives and success for social media that are relevant to business goals. The document provides examples of tools to measure engagement, brand health, and impact on business metrics like leads and sales.
Are We Getting Results? How to Track Your Nonprofit Social Media Efforts with...Julia Campbell
Effective social media marketing involves thoughtful measurement and analytics. This session will cover:
How to choose which metrics to track.
How and where to pull metrics from social media.
A brief review of popular analytics dashboards.
How to use a simple spreadsheet to manage tracking.
Benchmarking reports so you can see how you are doing compared to other nonprofits.
The document discusses measuring the success of social media. It provides an overview of the Wharton Interactive Media Initiative (WIMI), which brings a data-driven perspective to media businesses. WIMI distinguishes itself by focusing on the interaction between content providers and users, capitalizing on individual-level data. It is dedicated to understanding these complex interactions to drive new business strategies. The document then discusses challenges in social media measurement and provides examples of basic universal marketing metrics that can be used, such as followers on Twitter and engagement on Facebook. It emphasizes the importance of moving beyond "buzz" to understand real business impact.
How to leverage social media at IT organizationsThe Oren Group
The rapid proliferation of social media has revolutionized the way companies and consumers from around the world communicate, collaborate and transact. Social media platforms & tools like:
Facebook, Twitter, blogs, webcasting, and virtual events have positively impacted our business by enabling effective knowledge sharing & collaboration with various stakeholders across different geographies and time zones.
In this presentation, I will explain why and how IT companies should use social media to develop relationships with their target audiences and stakeholders.
I will discuss social media strategies & platforms that are available for companies in general and IT companies in particular to effectively provide value to their customers and stakeholders. Several case studies of IT companies that have successfully adopted social media are provided.
Every day, enterprises across the globe are engaged in two key activities: delivering effectual effects and building decisions that create impact. If you are in the big business of building enterprises that will be more valuable in future than present your decisions need to be driven by smarter data.
Companies today are witnessing a huge explosion in data availability - 90% of the world’s data was formed in the most recent years. Structured, semi- structured and unstructured data across internal business systems and external sources like social
media, market data and syndicated study are now creating an incredible opportunity to construct insights, therefore leading to intelligent decisions. However, as this data is generally available to an enterprise’s competitive set, only those who have a vision for
leveraging this intellect and are adept will eventually out-compete others.
STC 2010 Strategies for the Social Web for DocumentationAnne Gentle
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Vermarkterübergreifende Videostudie "Brands in (E)Motion"
Yahoo! Engagement Study
1. TECHNICAL REPORT
YL-2010-008
EDISCOPE: SOCIAL ANALYTICS FOR ONLINE NEWS
Yury Lifshits
Santa Clara, CA 95054
{lifshits@yahoo-inc.com}
December 20, 2010
Bangalore • Barcelona • Haifa • Montreal • New York
Santiago • Silicon Valley
3. EDISCOPE: SOCIAL ANALYTICS FOR ONLINE NEWS
Yury Lifshits
Santa Clara, CA 95054
{lifshits@yahoo-inc.com}
December 20, 2010
ABSTRACT: We present Ediscope — an system for measuring social engagement around online
news articles. Ediscope collects signals from Twitter, Facebook and Bit.ly. Using our link spotter
and social crawler we address a number of questions. What is a lifespan of a typical news story?
What are the typical engagement numbers per-pageview? Can social signals be used for pageview
estimates? How much improvement a social optimization can bring to a news source? Our first
results indicate that less than 20% of activity happens to an article after its first 24 hours. In average
a story has 5-20 social actions per 1000 pageviews. For most feeds, top 7 stories a week capture
65% Facebook actions and 25% retweets. The correlation between pageviews and social signals
is surprisingly low. Our measurements indicate a double digit improvement potential for social
optimizations.
4. 1. Introduction
Online news are on the way to become our primary source of information. In order to win the
competition and delight the users, the editors of online news have to constantly optimize their con-
tent strategy. Content strategy is a new applied discipline that addresses the following questions:
What should we write about? How many articles per day? How to allocate coverage shares be-
tween main topics? How to discover breaking stories? Which stories to promote within a website?
What is the most effective navigation structure for our content? Next to content strategy, there is
the emerging field of social media optimization (SMO): How to maximize engagement? How to
maximize secondary traffic from social sources (Facebook, Twitter)? How to grow the number of
followers, subscribers and fans?
To solve the problems of content strategy and social media optimization one needs both art
and science. As web news are inherently more measurable than print news, the role of science
is increasing. Until recently, most solutions were based on click-through rates, time spent, eye
tracking and pageviews. This information is typically available only for website owners. Therefore,
it was hard to create generic measurement and optimization solutions. Fortunately, in the last couple
of years, social signals emerged as a universal and public feedback mechanism. In this paper, we
present a study based on Facebook likes, links in Twitter, and clicks on Bit.ly links. The availability
of social signals for content strategy problems created the new research direction of social media
analytics [1].
Questions we address in this study: For how long an average article receives user attention?
Can we guess the pageviews counts from social signals? Can social signals be used to promote
best stories? Should editors focus on producing better content or on producing more content? How
much improvement can it bring?
Contribution. Our first contribution is the data engineering infrastructure we build for the project.
Ediscope system has modules for link discovery, signal monitoring, statistical analysis and visual-
ization. Ediscope data and lookup tool are available at http://ediscope.labs.yahoo.net.
Currently, Ediscope toolkit is available on request as it is subject to third-party API rate limits. Feel
free to contact Yury at lifshits@yahoo-inc.com to use Ediscope for your project or to order a custom
report on your favorite news source.
Our most surprising finding is the low correlation between social signals and the actual pageivew
counts. The gap is especially large for non-top new, in that case Pearson coefficient approaches 0.5.
To understand the role of these low correlations we introduce a simple user experience model. Under
this model we demonstrate a potential for double digit improvement at Gawker, Business Insider,
Change.org and Forbes blogs.
In average we see around 10 Facebook/Twitter actions per 1000 pageviews. Correlation between
social activities is higher for the top news than in the average case. Mainstream sources have much
more Facebook activity than mentions on Twitter. Tech media has the opposite situation. Facebook
actions are much more skewed to top news. Finally, Twitter signals have slightly better correlation
to pageviews counts.
5. Our results show that almost universally across news sources less than 20% of activity happens
after the first 24 hours. Feeds and frontpages drive attention to the latest content units. Search brings
traffic to “evergreen” content like Wikipedia. But there is no driver for materials with mid-range
(few weeks – few months) lifespan. Perhaps, we need a new promotion mechanism for this type of
content.
Remark on focus. When scientists work with real world data there are two mindsets. One can
focus on hard/intelligent tasks like model fitting and parameter predictions. This approach makes it
easy to judge the project by comparing accuracy of results to the previous work. The other method is
to measure the raw signals and turn them into actionable insights for domain experts. In this case, the
findings can be judged by novelty of measurements and importance of resulting recommendations.
This study follows the second approach. Here are our takeaway lessons for editors and product
managers of online news:
Create new promotion mechanisms for in-depth content. At the moment there is no middle place
between breaking news and reference content. Perhaps, we need dedicated feed, section and
frontpage module that highlight articles of mid-range lifespan.
Use social signals for content optimization. There is a serious gap between what content units are
most liked and what content units receive the most pageviews. In other words, user experience
can be improved by using Facebook likes and retweet counts to promote the most popular
content.
Check your engagement scores. If you see less than 10-20 social actions per 1000 pageviews,
your sharing functionality can be improved. Typically, it is as simple as getting the buttons of
the right size, at the right place and minimize the number of clicks to share your content.
Check your head/tail structure. If you have heavy head, improvements in quality and promotion
mechanisms should be your priorities. If you have heavy tail, your best opportunity is in
expanding content production. According to our measurements, one has heavier-than-typical
head if over 75% of weekly Facebook actions or over 45% of weekly retweets is concentrated
in top 7 articles.
1.1. Related work
Social signals (Facebook likes, Retweet counters, Bit.ly click counters) are relatively new phe-
nomena. In particular, Facebook Like button was introduced in May 2010, just 6 months prior to
this paper. Until now, social analytics research was centered around text-based signals [6, 7, 8]. To
our knowledge, we present the first temporal study of Facebook like counts.
Before social signals, researchers were looking into comment counts, Digg counts and Youtube
viewcounts. Tsagkias, Weerkamp and de Rijke developed several algorithms to predict the total
volume of comments shortly after publication [12, 13]. Paul Ogilvie measured and modeled total
comment counts across various RSS feeds as a part of FeedHub project [9]. Cha, Kwak, Rodriguez,
6. Ahn, and Moon performed a long tail analysis of Youtube and Daum videos [3]. Avramova, Wit-
tevrongel, Bruneel and De Vleeschauwer developed classifier that distinguishes videos with expo-
nential and power law popularity decays [2]. Salman and Rangwala showed how to predict a total
Digg count shortly after publication [10]. Spiliotopoulos studied correlations between Digg counts
and comment counts for most popular stories [11].
The key advantage of social signals comparing to comment/Digg/Youtube counts is their uni-
versality. Only now one can develop optimization/prediction/recommendation systems that will be
applicable to any news source on the Web.
2. Overview of Ediscope System
2.1. Architecture of Ediscope.
For our study we implemented a new social analytics system called Ediscope. It has four primary
components. Link spotting tool is taking RSS feeds as an input and check them regularly to spot
new links. In many cases, RSS feeds present proxy links in order to measure clicks from RSS
readers. In particular, Feedburner and Pheedo do that. In this cases we convert proxy links to
the original ones. The second component is signal crawler. It takes news URLs and calls public
APIs (Facebook, Bit.ly, TweetMeme) to retrieve the current numbers for a given story. We also
implemented custom scraping for pageview counts. After that, we have monitoring component
that re-crawl active links in our database regularly (by default, every hour). Ediscope’s monitor
computes the deltas to the previous crawl for measuring activity over the last interval. Monitoring
functionality is used for temporal analysis of social engagement. Finally, we call Google Chart API
for dynamic visualization of results at Ediscope’s website.
In its current form, Ediscope has certain limitations. First of all, APIs we use have strict rate
limits. In particular, TweetMeme only allows 250 requests per 60 minute time period. This forced
us to focus on smaller datasets. Secondly, the same news article can be represented by several
URLs. Sometimes, Facebook, Bit.ly or Twitter fail to recognize these links as the same object. As
a result, APIs return lower engagement numbers, missing likes, clicks and retweets on non-canonic
versions of an article. E.g. Wall Street Journal has different URLs for a story when you visit it
directly vs. when you visit it from the frontpage. Next, many top websites do not have RSS feeds
or their feeds do not work properly. For example, Yahoo’s today module, the central piece of its
frontpage, does not have a feed. In these cases, one has to use manual lookups or scraping. Finally,
Ediscope is using a pull mechanism to discover new stories. By the time we add an article to our
system, around 15% of its social activity has already happened. In the future, push mechanisms
such as PubHubSubBub can be used to address this issue.
There are several commercial systems in the space of social analytics. Postrank is a proprietary
article ranking algorithm that takes social signals into account. BackType is a lookup system that
retrieves the current values of social metrics. Unlike Ediscope, it does not have the fully accessible
temporal profiles or pageview extractor modules. Klout is using social signals to rate news sources
and Twitter personalities.
7. 2.2. Datasets.
We created three datasets for our study: temporal set, pageview set, head-tail set. For temporal
analysis we selected 10 RSS feeds from major US news sources. We used our linkspotting module
to discover 20 articles per source. Link spotter was checking RSS feeds every 10 minutes in order
to discover articles almost immediately after publishing. Then, we used our monitoring tool to
update social counts every hour and compute the corresponding delta values. As a result we have
got temporal social profiles for 20 articles at 10 sources. For pageview analysis we consider four
major content networks that explicitly show viewcounts at their articles: Business Insider, Gawker,
Forbes Blogs and Change.org. For every network, we picked three RSS feeds, launched our link
spotting module and kept it live until we spotted around 50-75 articles per network. Then we waited
for several days until the total social counts are close to their final values. Then, we used our crawler
to measure social counts and pageview counts for every article in our dataset. For head/tail analysis
we looked at RSS feeds of several major news sources. For every publisher, we used link spotter to
get all articles from a one week period (around 200 articles per feed). Then we crawled them once
to collect social counts.
3. Empirical Study
3.1. Article Lifespan
In our temporal study we track 20 articles from each of the following sources: Washington Post,
Gizmodo, CNN, MSNBC, HuffingtonPost, Yahoo News, New York Times, Engadget, Mashable,
and TechCrunch. On average, every story has 901 Facebook actions (likes, shares and Facebook
comments), 221 retweets and 660 clicks on from Bitly-shortened links. The following table repre-
sents percentages of activities for the first, second, third, forth and fifth interval of 24 hours after
publication. Note that the total share of activity is significantly less than 100%. This is due to ac-
tivity in the interval between the time a story was published and the time Ediscope has discovered
it. Out of all sources, Engadget articles have the slowest decay of activity and Yahoo News has the
sharpest decay.
8. Signals in average 1 day 2 day 3 day 4 day 5 day
Facebook 73.94 11.57 2.83 1.29 0.48
Twitter 70.71 5.11 1.72 0.69 0.37
Bitly 73.27 8.07 2.49 1.06 1.01
Engadget signals
Facebook 56.13 24.40 9.03 4.35 1.99
Twitter 71.27 9.24 4.12 1.28 0.71
Bitly 76.53 10.02 4.12 1.54 0.86
Yahoo News signals
Facebook 85.49 6.69 1.01 0.38 0.10
Twitter 84.80 4.21 0.33 0.13 0.00
Bitly 33.88 2.08 0.40 0.21 0.05
Figure 1: Average activity of Engadget article during the first 68 hours of track-
ing. Deep blue represents Facebook, light blue represents Twitter, yellow rep-
resents Bit.ly.
Figure 2: Social activity of Engadget article “BlackBerry users running out of
loyalty”
9. Here are our main observations:
• Majority (typically, over 80%) of social activity happens during the first 24 hours.
• Monotonicity. Majority of shapes are monotone or monotone after daytime correction (bump-
next-morning effect).
• Twitter is geeky. While mainstream sources like NYT, Yahoo, CNN, MSNBC and Washington
Post have up to 10 Facebook actions for one retweet, TechCrunch and Mashable have more
retweets than Facebook signals. The Facebook advantage over Twitter in mainstream news
indicates that it can be a more reliable signal for content optimization solutions.
• Non-original content has lower activity. HuffingtonPost has two patterns: one for original
posts, another for aggregated content. Five links from TechCrunch feed are re-posts from
CrunchGear and TechCrunch.EU and have much lower counts than TC-proper articles.
• User experience flaws. The sharing functionality can have serious affect on total amount of
activity. In particular, at New York Times Twitter buttons do not directly tweet the story, but
instead ask reader to use Twitter for logging into NYT.
The fact that most activity happens during the first day has serious implications for editors
and product managers of online news. As our study shows, the currently used mechanisms for
promotion (feeds, frontpage promotions, cross-linking) are only capable for driving the first day
audience. In such an environment, weekly/analytic/evergreen content is highly discouraged and
unsustainable. Thus, if a certain publisher wants to produce longer-lifespan articles, it should depart
from existing content promotion strategies. On a positive side, we feel that the opportunity of high
quality weekly/monthly analytic content is wide open in almost every vertical.
3.2. Per-pageview Statistics
Several online content networks display actual pageview counts. This allows us to compute
average amounts of social activity per 1000 pageviews. In some cases several top stories have
different activity pattern than the rest of the site. To get more robust results we compute averages
both for full sets of articles and the sets excluding top 10 articles.
Network Facebook Twitter Bit.ly FB (non-top) TW (non-top) BT (non-top)
Gawker 24.59 4.66 13.36 11.55 4.74 2.65
Forbes blogs 4.61 9.16 41.41 5.13 11.86 29.00
Business Insider 3.08 6.40 34.37 3.90 28.99 106.47
Change.org 4.43 2.74 3.54 8.69 4.12 6.25
Then we look at the Pearson correlation coefficient between social signals and the actual pageview
counts. We also compute correlations between Facebook and Twitter signals and between Bit.ly and
Twitter signals.
10. Network FB / PV TW / PV BT / PV FB / TW BT / TW
Gawker 0.92 0.95 0.93 0.95 0.95
Forbes blogs 0.35 0.40 0.63 0.34 0.63
Business Insider 0.93 0.54 0.65 0.65 0.87
Change.org -0.01 0.45 0.05 0.34 0.65
Excluding top 10 news
Gawker 0.47 0.63 0.41 0.47 0.35
Forbes blogs 0.12 0.34 0.55 0.31 0.56
Business Insider 0.34 0.43 0.53 0.50 0.80
Change.org 0.67 0.50 -0.09 0.47 0.75
To get a visual sense of correlations we present plots for Gawker and Change.org. Absolute
values are scaled to fit in the same space. The top-right point at Gawker plot is in fact far outside of
the chart (Gawker has one outstandingly popular story).
Figure 3: Correlation between retweets and pageviews at Gawker network
Let us make some observations from the above tables:
• On average articles have around 10 Facebook/Twitter actions per 1000 pageviews.
• With the exception of Facebook signals at Gawker, the top news have less social actions per-
pageview than the average stories.
• For the non-top news, correlation between social signals and pageviews is around 0.5. Recall
that Pearson coefficient is ranging from -1 (perfectly negatively correlated) to 0 (totally inde-
pendent) to 1 (perfectly positively correlated). Thus, 0.5 value means that social signals are as
close to perfect correlation as they are to total independence.
• In 6 cases out of 8, retweets have higher correlation to pageviews than Facebook actions.
• Change.org shows negative correlations in some cases. An article is more likely to get Face-
book activity if it has less pageviews. It turns out that “Social Entrepreneurship” section has
much more pageviews but the same (or even slightly lower) Facebook counts. Once we re-
move articles from this section, the correlation returns to positive value.
• As expected, bit.ly clicks are better correlated to retweets than Facebook signals.
11. Figure 4: Correlation between pageviews vs. and Facebook (dark blue), Twitter
(light blue) and Bit.ly (yellow) signals at Change.org. The gap in pageviews
represents difference in popularity between different sections of the portal.
Looking at our per-pageview results, one can try to reconstruct pageview counts for the rest of
the Web. The baseline guess would be around Facebook count (or Twitter count) times 100. As our
measurements show, there are more chances to accurately predict the pageviews for a top story than
to do so for an average article. And looking at our lifespan study, we recommend Facebook over
Twitter as the primary signal for mainstream sources.
What lessons can one learn from these measurements? At the moment the role of social traffic
in overall article success seems to be very small. For an average story there is a very low correlation
between social signals and its pageview count. When we include top stories to the picture, social
activity per pageview actually goes down. These observations hint that factors different from lik-
ability and social cascades are playing the leading role in pageview success. As a result, traffic is
allocated to not-so-likable stories.
Let us do the following mind experiment. Assume for a moment that Facebook count or Twitter
count represents the actual reader satisfaction score. Then we can compute the total user satisfaction
score as the sum of products between pageviews and Facebook/Twitter counts. Now, let us reallocate
pageview counts in a way that the top pageview value corresponds to the top Facebook count, the
second top corresponds to the second top and so on. Then, we can calculate the “optimal” user
satisfaction score. In other words, we want to check how much user benefit promotion-by-likability
can bring to existing content networks. Below is the table of our results.
Network FB increase TW increase FB increase (non-top) TW increase (non-top)
Gawker 1.019 1.026 1.330 1.181
Forbes blogs 1.566 1.403 1.796 1.341
Business Insider 1.047 1.342 1.402 1.227
Change.org 2.346 1.245 1.109 1.110
12. As we see, all networks have double digit potential to increase their user experience for non-top
stories. Forbes blogs and Change.org can significantly increase the overall user experience. Again,
Change.org results look a bit weird because it has two cluster of very different articles. One are
more likable, others get more pageviews. So the overall experience can be improved significantly.
Once we remove the top news (one cluster), the rest of the site can only achieve 10-11% increase. Of
course, our model for user satisfaction score is oversimplification, but it can be used as a first-order
approximation of possible improvement based on social signals.
3.3. Head vs. Tail Analysis
In our final experiment we collect links from RSS feeds at several US news sources over the
course of one week. These feeds have from 64 to 226 items per week. Then, for every source we
retrieve and sort the social counts for discovered articles. We compute the percentage of weekly
social activity that corresponds to the top story, top 7 stories and all stories outside top 7. We use
the constant 7 as a reflection of one-story-per-day strategy.
Feed Articles tracked Top item: FB / TW Top 7: FB / TW The rest: FB / TW
TechCrunch 182 32.3 / 4.6 61.5 / 16.8 38.5 / 83.2
Mashable 162 23.1 / 2.1 47.1 / 13.2 52.9 / 86.8
Wired 120 9.9 / 4.8 41.4 / 24.9 58.6 / 75.1
Engadget 200 44.3 / 18.9 68.7 / 27.5 31.3 / 72.5
Wall Street Journal 201 36.6 / 5.8 65.4 / 18.5 34.6 / 81.5
Vanity Fair 64 21.8 / 11.4 70.5 / 44.7 29.5 / 55.3
Yahoo! Upshot 109 28.8 / 26.0 75.7 / 59.1 24.3 / 40.9
Yahoo! Top News 226 20.9 / 9.1 45.6 / 29.6 54.4 / 70.4
All Things D 139 66.2 / 17.2 89.2 / 41.5 10.8 / 58.5
Gizmodo 82 36.1 / 5.2 70.0 / 21.1 30.0 / 78.9
Aol News 78 19.2 / 11.4 85.1 / 44.4 14.9 / 55.6
One can made several immediate observations:
• Typically, around 65% of Facebook actions and 25% of retweets happens around top 7 stories.
• Facebook activity is much more heavy-headed than retweets.
• Yahoo! Upshot is the most heavy-headed blog in our study. Only 40% of retweets and 25% of
Facebook actions happens outside top 7 articles. Perhaps, it is so, because Upshot has very few
dedicated readers, and majority of action corresponds to a few Yahoo-wide promoted stories.
AllThingsD is also fairly heavy-headed.
• Mashable and Wired have the heaviest tails. Both have over over 75% of retweets and over
50% of Facebook actions outside top 7 stories.
Let us offer an interpretation from a content optimization perspective. The heavy head of social
activity means that the total user satisfaction can be improved by improving quality of the tail
13. content or by finding a better ways to promote it. The heavy tail indicates that the tail content
has its own audience and is well promoted. Thus, the best opportunity for heavy-tail websites lies
in expanding its content production. For more accurate interpretation, one should track individual
consumption patterns. Goel, Broder, Gabrilovich and Pang have recently shown that the purpose of
the tail inventory is not only to capture new users but also to better serve users who like some-of-
the-top and some-of-the-niche [5].
4. Roadmap for Social Analytics
There is a number of natural next steps for Ediscope framework. First, we can turn our mea-
surements into rankings of news sources and individual writers by their engagement scores and
lifespans of their content. It is also informative to compare the signals for the same story covered
at different destinations. Then, one can do in-depth factor analysis to find what features of content
and audience increase the overall success of an article. In particular, what is the role of frontpages
and other in-site promotions? Another important step is to release datasets for research community.
Pageviews vs. social signals spreadsheet is likely to be published first. As we identified the problem
with content of mid-range lifespan, one should have a closer look at this area. Videos and products
have a longer lifespan and should be studied through social signals. And, of course, Ediscope should
collect larger datasets to make its findings more robust.
The general direction of using social signals for content management is wide open. Here is the
overview of the key areas.
Data engineering. Ediscope establishes the basic architecture for news analytics systems. For
a typical study, one needs content discovery, signal crawler, monitoring, statistical analysis and
visualization components. Looking into future, the research community will benefit from a shared
public stack of these tools. We do not want to recreate the same code again and again. Ediscope
platform can be extended in a number of ways. Of course, we need more signals: StumbleUpon,
Delicious, Yahoo Site Explorer, Digg, Spinn3r, comment counts, signals from public and private
hit counters. In its future versions, Ediscope can incorporate content metadata: author, publisher,
keywords, topics, headlines, tags, full text, content type, date and time, staff/guest/sponsored. User
data can be harder to add due to privacy concerns, but eventually it will be a part of analytics
systems. We need real-time content discovery and signal stream processing. Higher rate limits
should be negotiated with API providers. Then, there should be a way to add prediction, ranking
and optimization algorithms on top of basic infrastructure.
Measurements and modeling. Every category of web content can be a subject of social ana-
lytics: video, products, movies, books, websites, blogs, newspapers, magazines, TV shows, and
content farms. One can focus either on a particular vertical or on a content network (Yahoo, MSN,
Aol). A number of metrics can be created based on social signals: content lifespan, engagement
score, engagement-per-visit, share of social traffic to overall pageviews. Once we focus on a certain
content source and a metric, it is time for factor analysis. How do features of content, audience
14. and user interface affect the social success of a published material? Then, we need comprehensive
industry studies: the baseline numbers for social engagement and leaderboards. Finally, one can
create a taxonomy of engagement scenarios of content units.
Content optimization. Of course, the ultimate goal of social analytics is not just to collect data
and compute some metrics and rankings. The real impact is in using social insights for making
better publishing choices. Every online publisher faces the following issues: Choose stories and
topics to cover. Balance recency and importance in news coverage. Optimize headlines. Optimize
article length. Optimize in-network promotion. Rank its own stream of news [4] and make the best
selection for the frontpage. Find and fix underperforming areas. Optimize user interface. Make the
best content easy to discover. To conclude, the future of Ediscope and other analytics systems is to
recommend choices that maximize social engagement.
Acknowledgement. Author thanks Benjamin Moseley and Silvio Lattanzi for fruitful discussions
at the early stage of this project.
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