Sounding Off

An Interview with Taylor Hanson of HANSON / 3CG Records followed by Alex White, Faithe Parker, Rob Buell, Taylor Hanson, and William Gruger Discuss Social Data Use Cases in the Music Industry

Taylor Hanson and Chris Moody

An Interview with Taylor Hanson of HANSON / 3CG Records

During an intimate discussion with Taylor Hanson (3CG Records), we learned about some of the bigger challenges that the music industry has faced over the years with staying relevant and understanding consumer insights on a deeper level. Taylor Hanson, one of the first artists to really embrace social media in a big way shared his thoughts on why the music industry has been slow to change. In fact, before the days of websites for branding and social platforms existed, the Hanson brothers were already leveraging public forums and their fan clubs to find out what makes their fan’s really tick. To this point, in the early days on online marketing in the music industry Hanson’s website became the third largest trafficked music site next to MTV and AOL.

Being Part of a Bigger Ecosystem

By embracing fans on a deeper level and finding out what makes them excited, Hanson was able to discover how to build long lasting communities that existed beyond just a new record label and how to activate fans for real social change and activism. He retells an example of how a music single he released to promote medical supplies in Africa became a real movement where fans could participate in his music while donating to a good cause. These types of actionable results  was something revolutionary at the time.

Looking Ahead in The Social Landscape

Hanson attributed the music industry’s slow response to adopting cutting edge ideas and strategies to asking the right questions and finding out what consumers were really thinking about besides just the life of a song or CD. His suggestion for stakeholders in the music industry is asking the right questions in order to get to the right discussions taking place. For example by discovering artists inside and out – how their music is shared, who it’s shared with – translates into understanding how an artist fits into the larger space.

He also recommended big brands and artists alike need to collaborate more openly, do things with a purpose, and not be afraid to have authentic relationships and discussions with fans to keep them entertained and passionate about a topic. It’s this type of community building that keeps fans coming back and sticking around long term.

It’s All About Trust

Trust was one of the big theme in Hanson’s mind. In his final remarks he discussed how being able to connect fans, develop real community, and turning local outreach into real activism for change was the obligation for artists and music moguls to make change happen faster. When you have passion and quality that leads to trust which can enable you to build something special.

@hansonmusic @mralexwhite @faitheparker @reloadronnie and @wgruger are Sounding Off about social data in music

Alex White, Faithe Parker, Rob Buell, Taylor Hanson, and William Gruger Discuss Social Data Use Cases in the Music Industry.

Pernille Bruun-Jensen led a lively panel discussion with music mavens Alex White (Next Big Social), Faithe Parker (Marbaloo), Taylor Hanson (3CG Records), and William Gruger (Billboard) discussing some of the biggest challenges and take aways that social media has brought to the music industry.

For starters, Bruun-Jensen began with the question “what was your moment when you started to take social data seriously?” One of the ways that the music industry has evolved in the past decade is how artists are becoming found. Before artists were focused on pitching their music and who they were. Now it’s about being able to share your brand, who you are as a person and being able to engage fans on multiple channels and platforms (Vine, YouTube, Facebook, etc.). It ultimately means, how “well rounded” and marketable are you today so you can stay relevant.

Another example that was brought up was how recent mega hits like Gangnam Style or Harlem Shake became huge movements in music simply because of the user participation that was involved to drive engagement.

For Parker, her big takeaway was when award shows became fan voted – by Tweets, shares and mentions rather than just phone voted. It was no longer up to a board to decide but an audience – power was really given to the people. For Hanson turning fan love into activism was powerful.

What’s Working & What’s Next?

The future of social data was one of the most compelling discussions of the entire talk as brands and artists look for new ways to stay relevant and connected to fans in a big way. For example, Parker shared how positive sentiment and volume of conversations became a powerful data point in determining placement of artists in existing or upcoming tours was worthwhile. The crux of the conversation was about looking at the incredible range of social data available today and being able to translate that into actionable insights for future initiatives.

Predicting The Future

One of the biggest challenges that the music industry faces is being able to predict top talent and the growth of future artists. Many times, record labels only look to record sales without looking at the bigger picture and what the data is telling. For a long time, proving what your audiences wants to hear wasn’t the end goal , it was being able to control the space and dictating to fans what should be popular and relevant. Now artist and brand success, thanks to social media insights, is dictated entirely on the fan experience and being able to stay relevant long enough to make a memorable impact in their eyes.

How Millennials Are Shaping the Future

Millennials – another industry buzz word brought to life from brands has been a term often used to describe the largest segment of the workforce. Millennials today are the driving force behind the success of new and existing artists. Many are often times attached to one or two mediums like Instagram or Twitter and the way in how a brand actively engages with them can shape the success of how they are perceived in the marketplace. For example, Billboard today utilizes the army of fans for Arianna Grande and many other artists to complete surveys and contests to find out which musician reigns supreme in the eyes of millennials. By engaging fans through contests, Billboard is able to drive tons of traffic back to their site to find out who makes up these users.

The summary here is that millennials want to be engaged in deep and meaningful ways and not always when you are ready to engage with them.

Retention and Growth

Important to the future of the music industry and social data is analyzing the ways in which music is shared and explored internationally. For example, Rdio has seen a tremendous spike in the way citizens from Singapore, Brazil and elsewhere have adopted music locally and shared it via mobile phones. The influx of new ways for connecting via the web and mobile device has made it even easier to stay on the latest trends.

Understanding the true long term success of a brand or artist through social data has become even more difficult with the influx of new mediums and platforms for connecting with fans. Artists like Rebecca Black have understood how to move audiences from one social platform to another while being able to keep fans interested and excited. Harnessing these types of trends coupled with being able to analyze the data in realtime to create actionable insights for future campaigns will lead to keeping fans activated for the long term.

Academic Researchers on Social Data’s Critical Frontiers

Anna Lauren Hoffmann, Jim Thatcher, and Shawn Walker Discuss the Challenges of Creating New Frontiers in Academic Research Using Social Data

@annaeveryday @alogicalfallacy and @walkeroh discuss Social Data's Critical Frontiers

Academics and Social

The partnership between marketers and technologies has always been evident, but the impact of academics on the industry ecosystem is harder to grasp. Anna Lauren Hoffman, Jim Thatcher and Shawn Walker have gathered to discuss their work with social data, their thoughts on the social industry and the impacts across technology and advertising.

Hoffman focuses her work on the ethics and cultural studies of data including social data. Walker focuses on social and political participation, data archiving and the ethics of using social data. Finally Thatcher focuses on the spatial aspects of social data, examining the geo aspects of social data in the larger concepts of political and economic trends amongst other areas. While the academics focus on different applications of social data, they have overlapping focuses crossing ethics and challenges faced in data analysis. The academic perspective highlights the assumptions industry experts make and understand the impact on the entire ecosystem.

Blending of Old and New

Unlike the industry experts, academics focus on longer-term projects, stepping away from the cutting edge and utilizing their deeper knowledge set on research models and traditional knowledge to better understand social data according to Thatcher. Discussing the value of academic research for tech, the key theme Hoffman, Walker and Thatcher hit on was the value of understanding the larger social theories when approaching and constructing analysis of social data. The number of assumptions created in social data is not always inherent to industry insiders but can greatly impact the outcome of the data we receive and analyze. Academics and the students they train who will be entering the workforce, work to better understand these skews constructed into the design and output of social.

One area in particular that the academic work is seeking to answer is the accurate capture and storage of social data. Simply capturing the text won’t supply a full picture; the accompanying metadata is equally as important and far more difficult to capture. Walker is focused on utilizing library archival techniques to understand how to “freeze” data for future analysis without loosing the integrity. However, his work impacts across academic and industry focuses, helping to push forward our ability to understand how data decays and work to resolve. As the industry grows, it’s ability to analyze data must take into consideration the liquidity of social data and the challenges faced in gathering accurate snapshots.

Ethics and Privacy

A shared area of concern for academics is the area of ethics. Across the industry, growing concerns over privacy invasions of analyzing social data have been at the forefront of discussions and it is no different within the academic world. However, within academics the discussion looks beyond open and closed data to understanding how traditional research consent agreements can translate to online and the challenges being faced. As Walker points out the data received within social straddles human and technology data and disrupts current models. Primarily, the mass analysis of data provides far more powerful insights with potential harmful implications that transcend data anonymity. Our current thinking is if it’s being publicly shared there shouldn’t be any issues, but given the massive scale we work with the logic begins to fall apart. Citing anonymization of data as a safeguard against privacy invasion doesn’t work according to Walker. The anonymization falls apart as soon as you start studying the structure of the network or combining the data with other sources, which is primarily where focuses of studies are. Hoffman points out the importance of context when examining online privacy. We tend to think of privacy concerns from one example and fail to expand our considerations to various situations that each pose unique challenges that must be addressed.

Another challenge faced within online privacy and consent is explaining the data users are agreeing to share. Often there is a technology knowledge barrier in fully explaining what users are sharing when they agree to provide social data for analysis. That challenge falls upon the industry to properly convey and help users understand what data is collected about them from social and how that can be used for analysis.

Looking to the Future

The challenges academics face in accessing data and recourses are numerous. Funds, technology to host data and skills to analyze are all limited and put constraint on the projects academics can undertake. All three panelists highlight the need for academics, both on an individual and institutional level, to work with the social industry to democratize data and create forums where collaboration can occur. There are many academics with impactful ideas who do not have the capabilities to properly access and manage social data on their own and it is a loss for the entire industry when those ideas go unanswered. If the industry can work with academics to increase data access, a partnership can exist where the industry can supplement it’s knowledge with the academics deep expertise, and help train the future workforce learning from academics on how to work with this data before graduation.

Driving Actionable Insights

Amy Lamparske from 3M and Kim Yarnell from Macy’s Discuss How Brands Are Using Social Data to Drive Business Impact

Amy Lamparske and Kim Yarnell

Amy Lamparaske, head of global social media at 3M, was joined by Kimberly Yarnell, Vice President of digital media at Macy’s, to provide insights on how social data within the enterprise can be used to drive tremendous business impact.

Amy and Kimberly agree that social data provides a limitless source of value tied to brand decisions that are being made to bring magic to the customer. At 3M, they have just undergone a large project to pull a year’s worth of social data to listen to the shift in conversation and understand how the brand is being perceived. Using these insights, 3M hopes to effectively connect with niche communities that are more difficult to identify and find more engaging ways to insert themselves into relevant conversations to reach consumers. 3M has historically been seen as a house of brands but is now viewing themselves as a branded house. Through the use of social data, they have found that the more the customer knows about the house of brands, the more loyal they are to 3M. Touching the average person’s life at least 100 times per day provides a massive opportunity for 3M to enforce familiarity as a catalyst to drive sales and brand loyalty. At Macy’s, Kimberly mentioned that they are looking to use social data from a predictive perspective to assess what market opportunities are available. While social data provides a limitless source of value, Kimberly fears that there is too much data to harness. The trick is to unlock the data at the right level of granularity so that it is not too overwhelming and can be manifested in a scalable way.

Challenges within Social Data

Many brands in today’s world have experienced challenges when it comes to adopting the use of social data in the enterprise and 3M and Macy’s are no strangers to this obstacle. While there is a huge hunger for social data within the enterprise, Amy and Kimberly agree that it is not about selling the value internally, but rather about making it easily accessible so that it can be used to influence key business decisions. At 3M and Macy’s, partnering with key executives within the organization is crucial to the influence social data has across the company. At Macy’s, Kimberly has implemented a reverse mentoring program in which her team keeps management up to speed on the innovation taking place across the various social networks in order to drive familiarity with the platforms.

At 3M, Amy mentioned that the larger organization has made huge strides towards harnessing insights from social data to institute change, but that it is a learning process. She believes that social data is powerful and what attracts her to the space is the amount of impact that you can have when you are close to the data and allow it to change the way you do business.

Amy and Kimberly unanimously agreed that the biggest challenges they face as internal advocates of social data is the lack of standardization that exists. To socialize brand performance internally is a giant feat. Trying to tell a story takes a lot of commitment and as internal advocates for 3M and Macy’s they posed a challenge to the audience of innovators to help them conquer this obstacle in order to allow them to unlock more spend and propel their brands forward.

Dreams for the Future

Fantasies of a constant feedback loop excites the minds of Amy and Kimberly. They hope that one day it will be easier to aggregate data from social, as well as all other internet sources, into one picture to provide actionable insights. This is where they believe the real opportunity lies. While 3M and Macy’s are both global organizations, they reiterated that their teams responsible for driving the value of social data are small and do not have the luxury of mining data to its full potential.

For Amy, she hopes to leverage social data into innovating products as this would provide 3M with a huge competitive advantage. As for Kimberly, making it easier for marketers who are time strapped to be able to harness the power of the data to drive decision-making would be a dream come true.

Navigating Your World One Reviewer at a Time

An Interview with Justin Overdorff from Yelp

Justin took the stage with Chris Moody to talk about Yelp and the platform’s unique data. Chris shared that one of the things that brings everyone in the room together – competitors and peers alike – is talk about the platforms creating this social data. Justin shared that Yelp’s goal is to connect customers with the business around them. He said that Yelp is search and discovery for local business. Yelp is now in 33 countries with Malaysia, and they are actively growing in additional international markets. Justin said that MAU’s are a key measure of their success, as is overall review counts.

Chris asked how Yelp uses data internally, and Justin shared that the structured business listing data (name, phone number, address, etc.) is sourced along with certain open-ended answers provided by the SMB’s themselves. (ie. kid friendly, wheelchair accessible, etc.) While the latter can be somewhat unstructured, their bread and butter unstructured data comes in the form of the long-form user-generated reviews on the site. This data is extremely valuable not only to customers looking for the nearest good restaurant, but lots of businesses are interested in this data as well.

One example can be found in financial services. Justin shared that hedge funds and investors ask Yelp for review data around companies such as Starbucks and other large brands to judge the sentiment around them. Another example is in those alternative lending institutions that put together loan packages for SMBs. A use case that Justin never expected which was brought to Yelp when they opened the doors to their data was in commercial real estate. He said that investors in real estate would use the geo-relevant data and sentiment of reviews to decide where in the city was an up-and-coming area worth investing in. He also mentioned that Yelp data can be a very valuable input when measuring the viability of a small business.

Chris next asked about geo-based data. Justin shared that when you search for sushi in a specific city on Bing, you get branded Yelp reviews back in the returned data. National brands also use Yelp data both in dashboard-format as well as raw JSON feeds to understand what is being shared about their products and offerings. Justin then shared that advertising will always be their core source of revenue, but monetization of the review data is another growing opportunity for Yelp. To point, he said that the “doors have really blown wide open” on Yelp’s data business, and nearly a third of the audience raised their hands when Chris asked who would like to work with Yelp data.

One of the really unique things about Yelp data is how self-correcting it is. With over 140M active monthly users, the folks reading and writing reviews actually update information for them. The SMBs also contribute to keeping the data up to date, and as a result you are always reading the most current information about a business in its reviews.

Chris asked about entrepreneurship and where they were seeing new opportunities. Justin shared that Yelp has a Small Business Council made up of SMBs from across the country. Once a quarter, these council members come to Yelp HQ to share feedback around the platform and its features. Yelp gives the SMBs many tools to improve not only their listings on Yelp, but business as a whole. From a best practices perspective, Justin shared that it really is unique from one business to the next. Sometimes not answering a particularly ugly review is the best course of action to pursue.

Once the audience was able to ask questions they started with a question about Beacons and how they might remove friction from engaging with businesses. Justin said Yelp is constantly looking at new types of technology and hardware, and that there is a cost-factor that needs to considered with Beacons. The battery life right now is another limiting factor of the technology, and they want to see Beacon hardware last more than just a few months. On a whole, Yelp is still a few years away from investing heavily in Beacon technology while the hardware itself matures and improves behind the scenes.

The audience then asked about fake reviews and how Yelp addresses these. Justin shared that they have a team of PhDs and spend a large amount of resources to filter out fake reviews. They don’t delete them, but rather don’t recommend them so they don’t appear above the fold on the review page.

The final audience question was about the permanence of reviews. When a company starts out, an early negative review can leave a lasting “black eye”. Justin shared that recency is important to remember, as the reviews and reviewers take care of themselves if you have honestly improved. This self-correcting behavior shows that a business has truly bettered itself since the initial negative review, but it is important for the users to see this as well. A positive consumer experience is Yelp’s key driver, and they work hard to maintain the best one possible.

Justin Overdorff and Chris Moody at Big Boulder

Data, Ads, and Twitter

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The Changing State of Twitter

Given the recent news of leadership changes at Twitter, a key focus of the discussion was on how this change will impact the company and shape the future of Twitter. The Wall Street pressure has shifted company cadence into a quarterly cycle, driving innovation across both the product and data sides of the business and highlighting opportunity areas for growth and monetization. The changes in both product and data cross industries, growing partnerships, utilizing data insights across all areas of business and expanding the reach of Twitter conversations. All of which are aimed at growing the power of social data and monetizing opportunities to leverage that power.

Reaching the Logged-Out Audience

While Twitter has built it’s success upon the engaged and responsive logged in audience, sharing and creating content, the company has now begun to focus on the massive audience that is not logged in. Realizing the initial barriers to entry, primarily around feed creation and curation, the platform is shifting to become more entry user friendly. “Instant Timeline” or feed creation based off of a new user’s contacts is a new product built to help tackle this issue. Another is the “While You Were Away” feature, built as a recap function to help you view the best content that was shared since your last login. In addition, the highly publicized Google deal is focused on growing the reach of Twitter content beyond the logged in audience and placing it into the hands of all online users. The partnership promotes the wealth of information shared on Twitter that online audiences are searching for, tying existing conversations and information to curious audiences outside the platform.

Harnessing the Data

The data side of the Twitter business is also rapidly growing. Capitalizing on their open source platform which allows for deep row by row analysis, rather than other broad aggregated sources, the Twitter team is working to answer the growing questions being posed. In their many discussions with CMOs across industries, the common theme is a 180-degree shift into data and insights. These CMOs see their teams across the business utilizing Twitter in different ways and need help in understanding the current status quo with their business, understanding the issues they are currently facing and receiving insights on how to resolve those. This shift partnered with the changing marketing environment, from the traditional funnel to a looped environment, with data inputs stemming across all points, the insights we can draw from social data are practically limitless.

It’s a Group Effort

Given the questions Twitter faces from customers across business areas there is an overflow of ways to innovate and draw insights from the wealth of data being produced by Twitter audiences across the globe. While Twitter is focused on answering these questions they are a single entity and are relying on industry partners to help them answer, innovate and create value with their data. Many of the successes they have found in utilizing their data has come from partnering with third party companies from start ups to massive consulting firms like IBM. Recognizing the need to democratize data, Twitter is focusing on improving ways to get developers the data they require, hinting at an upcoming announcement surrounding an expansion of Twitter data availability, specifically addressed at providing developers with more and broader data.

All in All

Twitter remains a key social channel and data partner in the industry. Their ability to innovate product to enhance user experience and grow data driven insights will help move the industry forward. Enabling increased value of social for users and customers benefits non-profits, start ups, academics, social analysts, data scientists, CMOs and day to day marketers. How all of these constituents partner and work together to utilize these opportunities will greatly determine the future environments and how this industry can shape the world for the better.

Social Data Law, Policy, and Ethics

Don Heider, and Judy Selby Discuss the Gray Areas Around the Ethical Use of Social Data

 @donheider and @judy_selby Discuss Social Data Law, Policy, and Ethics

Why Privacy Matters

Privacy and data seem to go hand in hand. With the emergence of more social platforms on the rise, the challenge that organizations, businesses and non-profits face is being able to share this data collectively and responsibly. Some have actively discussed how there is a growing need for a code of ethics around data privacy that needs to be enforced and shared amongst all parties that hope to access the data. By having such a document outlined it makes it easier to safeguard the privacy of users whose data is being stored and shared amongst third party organizations. This leads to a bigger discussion around privacy laws especially with social networks that access third party sites in order to create accounts and content.

Next Steps in Data Protection

What are the next steps for protecting consumer information and ensuring personal data is being shared for the benefit of everyone? This is a contested debate amongst private companies, the government, and academics and nonprofits alike. While there may not be one clear answer, clearly having an idea and addressing the common problems that might arise is a good first step. According to Don, ethical discussions are not meant to be black and white. One thing is clear though, organizations of all sizes have a responsibility for ensuring personal information is safeguarded. Take an example that Susan brought to light; Borders who sold customer data with regards to the types of books consumers purchase may not seem like a big deal but when it infringes on the privacy of certain parties or individuals based on their medical conditions as an example, it becomes a bigger dilemma.

Responsibilities for Everyone

Examples like Borders are why it’s important when developing any kind of policy or regulation to ensure the primary stakeholders are involved in the discussion beforehand. By getting ahead of the discussions before issues arise, companies and organizations can begin to identify trends and core issues that could potentially be damaging if not addressed sooner.

Failing to address issues of consumer data privacy not only breaks the trust of the consumer in the company they have come to buy from but also creates mistrust of the larger marketplace. A result of  breaking this trust is that some consumers have decided to shop with competing brands rather than buying from a company they’ve used for years.

Along these lines, the major questions that some organizations consider when it comes to data is:

Is it a benefit to the customer that we collect the data or just to us? And if we woke up in the morning to see this in the news would be happy about it?

Our Collective Obligation

Any time consumer data whether our own or someone else’s is at stake, it becomes the obligation of all parties to determine how to best safeguard it from falling within the wrong hands. By having these clear terms of service on websites, social media platforms, and other places it aides in creating the level of trust consumers are expecting on behalf of organizations of all sizes.

EMBERS

Parang Saraf from Virginia Tech Gives a Pecha Kucha Talk on Building Tools for the Future

Parang Saraf from Virginia Tech Gives a Pecha Kucha Talk titled EMBERS on Building Tools for the Future

Discussing a four-year $20 million IARPA funded project that spanned 80 researchers at 13 universities and 3 companies, Parang Saraf details the flagship social media project led by Virginia Tech. The goal of EMBER was to take a plethora of data sources, parse them through a series of machine learning terms and systems to ultimately create asystem predictive of disease outbreaks and protests.

Run as a forecasting tournament, the Virginia Tech-led team was crowned the winner after two years of the project. In order for Saraf’s team to succeed they not only used big data, but also wide data, stemming from many sources including social and online data. Saraf’s team used more unconventional sources such as satellite imagery and restaurant reservations. In the case of the satellite imagery the team examined hospital parking lots to measure the number of cars in the parking lot, while restaurant reservation counts were tracked over time with both sources helping to impact flu trend prediction.

The project was able to successfully predict protests in Brazil, Venezuela, and Mexico 3-5 days in advance, leading them to win the research competition. However, once the project ended, Saraf’s team faced the challenge of taking academic research and using it for commercial applications.The team is exploring themes across different industries.

Potential examples include helping financial industries predict commodity exchange rates and price trends, predicting elections across countries, forecasting cyber attacks utilizing social and online data to make predictions about emerging technology, and within brands to create popularity trend predictions for brand growth. In the last category, the team found nuances across social sentiment’s impact, realizing the predictive quality was limited for brands but highly impactful for entertainment in predicting a TV show’s popularity.

Looking to the future, Saraf and his team are excited to continue growing the applications of their product within commercial industries. Enhancing the predictive power of social data in combination with data sources both big and wide is their ultimate goal.

Managing the Seedy Neighborhoods of Social Data

Sean Gorman from Timbr.io Gives a Pecha Kucha Talk on Where Free Expression Collides with Harassment

@SeanGorman from Timbr.io Gives a Pecha Kucha Talk on Where Free Expression Collides with Harassment

What is Pecha Kucha, you might ask? Wikipedia has a great explanation here, but in a nutshell it is a presentation style where the speaker works through 20 slides and spends only 20 seconds on each. During an afternoon session at #BigBoulder 2015, Sean Gorman led the audience through his version of a Pecha Kucha talk that focused on the fine line between free speech and online harassment.

Sean opened his 6 minute, 40 second presentation (remember, 20 slides & 20 seconds each) by sharing that it is important to talk about the negative sides of social data as well… mainly harassment and how it butts up against freedom of expression. When free speech goes too far, it becomes harassment and #Gamergate was an example of this. Twitter really stepped up to crack down on trolls in a number of ways including allowing for sharable blacklists.

So how do you find these bad actors in a crowd? Machine learning is one way, but you can get false positives. As such, it is important to get humans in the loop for that extra level of judgement, especially when you are talking about  taking a serious action such as kicking someone off a platform. Twitter open-sourced Black Raven to help crowd source and mechanical turk these type of assessments.

So how do you keep them off of the platform once you identify them? Bad actors quickly pop back up and serial abusers set up new accounts to get back to their harassing behaviors. So how do you find the returning bad actors? Fuzzy matches to banned lists are one way, looking at their previous friends who they want to reconnect with is another and understanding the harassment graph is a third. You need to understand if harassment is interconnected, especially when it involves users ganging up on a victim. These instances need to be prioritized to the top to be addressed quickly.

Is there a role for harassment guidelines and best practices in the Big Boulder Code of Ethics? According to Sean: yes, there is.

Partners, Platforms, and Data

An Interview with Bob Rosin from LinkedIn

Bob Rosin and Chris Moody

Bob Rosin is Vice President of Business Development for LinkedIn where he leads partnerships and platform for the company worldwide. Bob described that at its core, LinkedIn is about economic opportunity. This big vision is no easy feat, but LinkedIn is keen to tackle this challenge and is achieving success through connecting all of the world’s professionals.

Content is King

LinkedIn has proven to have a solid business model through a wide range of deals from large-scale programs, inShare buttons on websites, API programs, and partnerships. LinkedIn has three major revenue streams, recruiting accounts for 61% of the business, with marketing solutions and premium subscriptions equally accounting for the remainder. Advertising for the company is substantial and growing fast. The tech giant is quickly learning that content is king when is comes to fostering a healthy business. With LinkedIn representing the largest professional network in the world, individuals are looking to establish themselves as thought leaders. To grow their presence as a publishing platform, LinkedIn recruited 500 influential people and gave them a badge so that they could essentially blog in their area of expertise. This experiment was extraordinarily successful and has since been opened up to an additional 250 million influential members. Bob mentioned that the ability to place ads within these long format articles targeting niche audiences has proven to be very powerful.

From a metrics perspective, LinkedIn is moving away from measuring page views and is moving towards refocusing their product teams on measuring the value they are delivering to the member or customer.

You’re Hired

 If you are coming to LinkedIn to look for a job, you are not alone, and LinkedIn is rooting for you to convert at a rapid pace. Bob said, “If you come to LinkedIn looking for a job and you have to login every day for six months, that is a failure.”

Once LinkedIn has received a signal that a member is seeking a job, they can track how long they have been searching on the platform and track if that member was hired through various integrations. Through the acquisition of Lynda.com, LinkedIn is fulfilling their pledge to shorten your job search by closing the gap between what skills a member possesses and what skills are actually required to obtain a desired role. Based on gaps that exist, LinkedIn will recommend courses from Lynda.com to equip the member with the skills needed to hear the words, “You’re hired!”

Access to LinkedIn Data

About five years ago, multiple social platforms opened up their APIs and LinkedIn followed suit. They wanted to understand what the applications were for using LinkedIn’s data and it turned out that there were specific use cases that were beneficial to the business but they were outnumbered by use cases that were competitive or harmful to the platform and their users. To address these concerns, LinkedIn made significant changes to their Terms Of Service that were imperative to protect the company and their members. As an example, Bob described one of the biggest misconceptions being around ownership of the data on LinkedIn. He states, “People come with the notion that you can buy LinkedIn’s data, but it is not our data, it is our users data and we have to be careful with this personal information.” Now, LinkedIn’s APIs that are readily available are only the ones that the company is comfortable promoting. For use cases that fall outside of this realm of comfortability, LinkedIn has put a team in place to evaluate opportunities at determine if the company will grant more sensitive API access or not.

“X” Marks the Spot

Eric Gundersen, Javier de la Torre, Sean Gorman, & Francesco D’Orazio discuss social data and geo.

We are at the point in social where there is a shift happening in the way we look at data. Literally. Data visualization is becoming more necessary and more powerful. This panel showcased the ability to use maps to simplify analysis and make things more visual, but more importantly it makes it easier to separate the noise from the signal in social data.

Sean Gorman from Timbr discussed how this company focuses on the backend of data analysis and visualization. They are a platform for enabling algorithmic orchestrations with social data but he paraphrased this as giving people the tools to structure and clean social data to facilitate easy enrichment and binding of that data. Some enrichments around location he mentioned were friend-of-friend triangulation, finding location in text of Tweets, Gnip’s Profile Geo, and other ways to get location appended to social activities. He also pointed out that standard dashboard analytics providers want to add maps but don’t have the resources so they use Timbr.

Sean spoke about how there are anomaly detection algorithms as well as other algorithms on git and other places that they enable people to use and customize in their map making process.  That map making process on Timbr includes live iteration of code to maps for quick visualizations.

All of the panelists on stage are working to lower the bar for coming up with custom tailored analytics for the questions people have with social data.

Part of custom tailored analytics is the visualization aspect which numerous panelists hit on at different times. Eric from Mapbox showcased their product which is a platform for designers and developers to make custom maps within their apps. Companies such as Foursquare, Pinterest, and the Financial Time use Mapbox to display their data. Eric showcased a number of maps made in conjunction with Gnip which show 3 billion geotagged Tweets and pointed out that the most amazing part of these visualizations is that there is not map behind the dots, the data is actually what is creating the maps.

Mobile Devices on Twitter in Washington DC

Eric pointed out the analysis that can come from these types of visualizations. You can see the economic disparity in cities by looking at the regions where people post from an iPhone or and Android device. You can see the buying trends for countries such as the Blackberry usage in Malaysia, but nowhere else.

The panel also discussed how low opt-in on platforms for geolocation is an issue for creating great analyses. Only 1-2% of people opt-in t share their location along with their Tweet. Those who do share their location end up speaking for larger population when the analysis is done through a map visualization, which ends up creating a bias. Sean mentioned a preliminary study using 100,000 users that shows that the portion of Twitter that shares location is skewed to have a higher proportion of African Americans, a lower age, a higher proportion of renters, and a smaller household size than the general population. They are expanding the study to 1 million users.

Javier from CartoDB showcased a few visualization he made in conjunction with Twitter which show both a map and time in a single visualization for millions of Tweets. He talked about how social activities happen in a place which is important to see but also the concept that social activities happen at a time and that adds additional context to analysis. These visualizations allow you to explore the connection of time and place to understand how an idea spreads on social, he used a Beyonce surprise album release to showcase how Twitter “explodes” with news. Ideally for Javier you don’t have to be a designer or developer to tell these types of stories in powerful ways.

Sunrises vs Sunsets on Twitter

The panel also discussed the amount of conversation dedicated to data analysis but that there is not much talk of data visualization. Javier said that investment in the analytics and not the visualization is like having lots of power without any control.

Francesco from Pulsar talked about how they like to add in another filter, audience intelligence, to their analysis. He mentioned the example of people on Twitter talking about Coca-Cola is interesting but what are moms saying about Coca-Cola. Adding in this additional layer adds valuable context to the analysis.

He showed a map of people complaining about bad cell phone signal. A mobile carrier knows that when a network goes down it doesn’t happen in an instance but rather as a series of slow failures. Showing the maps of people complaining can allow the carrier to see where the network is failing and do something about it, which is a great example of social data being used in an engineering case, and not just for marketing or PR.

Conversation turned to geo outside of the USA and on other platforms besides Twitter. Sean mentioned that 10% on Sina Weibo activities have location attached, likely because of the emergence of phones along with emergence of internet in China and Weibo being a mobile first app. This lead to a discussion on how to incentivize people to share their location more. While issues like privacy were touched on it was clear that the panelists agreed that the biggest challenge in getting people to share location more was the lack of clear benefit to the consumer for doing so. Right now the conversation is more focused around how the industry can benefit from this resulting geo data and not on how the user can benefit from sharing it.

Many times in this panel the panelists mentioned that there is lots of challenges in the social geo space but also lots of opportunity. Javier said the next 12-18 months should be incredibly interesting in this space, as he touched on ideas around ability to control zoom and speed on maps that have time and place dimensions.