Social Media and Intellectual Property Cases

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Case Precedents

Verifiability/replicability

LVL XIII Brands v Louis Vuitton

In the case of LVL XIII v. Louis Vuitton in a trade dress case, Louis Vuitton was granted the motion to preclude the expert testimony of Dr. Coleman representing LVL XIII. The testimony was based on observational and analytical techniques of social media and online press coverage. His analysis consisted of reviewing over 100,000 unsolicited online posts and produced copies of 12 posts as evidence to the court. Coleman failed to document his methodology and failed to retain a copy of the search terms used, a compilation of the search results, or a list of discarded sites. Additionally, Coleman could not identify a potential error rate for his methodology nor establish the parameters in which he selected social media posts. Due to the unreliability of Coleman’s methodology and inability to replicate his results, the court dismissed his report and testimony.

Viking Yacht Company v. Composites One LLC

The Viking Yacht Company sought damages for actual, estimated and future potential repair costs from Composites One LLC. The Viking Yacht Company provided experts who testified due to their experience and extensive knowledge in the luxury boat industry that the 953-gel coat provided by Composites One LLC has and will continue to affect the plaintiff’s business. In Viking Yacht Company v. Composites One LLC, the court concluded in agreement with Composites One LLC, that under Rule 702, the yacht industry experts’ testimonies lacked a “verifiable or replicable methodology” and were therefore not considered expert reports and rather expert opinions and therefore excluded as being unreliable.

Moroccanoil v. Marc Anthony Cosmetics

In the case of Moroccanoil versus Marc Anthony Cosmetics, Marc Anthony submitted Facebook posts as evidence to the court of consumers referring to argan oil as Moroccan oil generically in Moroccanoil v. Marc Anthony Cosmetics. Moroccanoil objected to the social media posts as hearsay and unauthenticated evidence. While the court overruled the hearsay objection, agreed that the screenshots from Facebook had not been properly authenticated.

Secondary Meaning

Paramount Farms v. Keenan Farms

Paramount Farms had filed suit for trade dress infringement against Keenan Farms and their use of pistachio barrels in grocery stores in Paramount Farms v. Keenan Farms. Paramount Farms presented social media evidence in the form of the Facebook following of the Wonderful Pistachios brand to support their claim of fame. The court found that nearly 300,000 “likes” lent credibility to the other evidence presented. While the popularity on Facebook did not conclusively demonstrate recognition of the associated trade dress, it lent credence that the trade dress had become famous.

Kreation Juicery v. Eiman Shekarchi

Kreation Juicery filed a complaint asserting that Creation Grill had infringed and diluted their trade dress rights by copying their “living wall” décor that was featured inside their restaurants in Kreation Juicery v. Eiman Shekarchi. In order to establish secondary meaning of their décor, Kreation submitted hundreds of photos of Kreation’s food and décor published on social media. Additionally, Kreation submitted posts revealing congratulatory remarks in regards to opening up a second location, Creation Grill. The court did not find the social media posts were proof enough to establish distinctiveness, as other restaurants within the same Los Angeles market had also incorporated reclaimed wood in their décor.

Penhurst Trading v. Zodax

Penhurst filed claims for trademark and trade dress infringement against Zodax’s sale of infringing glassware in Penhurst Trading v. Zodax. Penhurst provided numerous social media posts as evidence of secondary meaning, as well as included posts consisting of #berryandthread which referred to the design at issue. Based on the evidence, the court declined to label the infringing trade dress as exceptional.

Predator International v. Gamo Outdoor USA

Predator International sells air gun pellets with red polymer tips. Predator claimed that Gamo Outdoor USA had infringed on their trade dress with the release of the “Red Fire” air gun featuring red polymer tips as well as copyrighted the copy in the marketing materials in Predator International v. Gamo Outdoor USA. Predator presented the court with consumer comments on YouTube videos that has mentioned the red tips, but they were not considered sufficient enough to prove secondary meaning. While the court acknowledged that the red tips had an aesthetic quality, they did not prove the designation of source.

Strength of Mark

Kibler v. Hall

In the case of Kibler v. Hall, DJ Logic brought trademark infringement and dilution claims against another artist known as Logic. DJ Logic had presented his substantial following on Twitter and Facebook as evidence of marketing the brand. On appeal, the Appellate Court explained that the district court had originally erred in concluding that the social media following was not proof of marketing. They pointed out that the “promotion on platforms such as Twitter and Facebook not only constitute marketing, but is among the most popular and effective advertising strategies today.” They court further continued to explain that a convincing case should have included evidence from Twitter and Facebook including “likes”, “followers” and “re-posts.” In order to show widespread public awareness, the appellate Court suggested looking into the number of and what kind of followers did DJ Logic have? How many of his followers were likely to re-post his messages and if any were celebrities? This type of analysis would suggest that the public was aware of his work and trademark.

Likelihood of confusion

You Fit v. Pleasanton Fitness LLC

You Fit brought claims of trademark infringement, dilution and unfair competition against Fit U in You Fit v. Pleasanton Fitness LLC. You Fit provided Yelp reviews to demonstrate that at least one consumer was actually confused about the difference between You Fit and Fit U. The post consisted of language stating that the two gyms were “very similar” and detailed account on the reasons why the gyms could possibly be the same. The court ruled that the anonymous Yelp reviews were not conclusive as evidence of actual confusion, they would be considered of potential consumer confusion and weighed in favor of the likelihood of confusion.

Bulman v. 2bkco, Inc.

In Bulman v. 2bkco, Pinweel asserted trademark claims against a similar photo-sharing company known as Pinwheel. The plaintiff presented the court with Twitter conversations in which confused consumers had downloaded the plaintiff’s app, but had contact the defendant via Twitter for help logging in. The defendant objected to the posts as “self-interested declarations,” but the comments, coupled with articles of frustrated customers proved to be sufficient evidence that the public is confused as to the source associated with the at issue mark.

Kind V. Clif Bar & Company

In the case of KIND v. Clif Bar & Company, KIND sought to prevent Clif Bar from using their trade dress. KIND submitted social media evidence such as “I love all things Clif and I’m sure I’ll like these too, but don’t they look like Kind bars?” The court ruled these posts as unhelpful as claims of similarity do not establish consumer confusion. However, posts similar to “I was about to pick up one of those MIJO Bars because I thought it was a KIND Bar at the vitamin shop…” showed initial confusion and weighed in favor of the plaintiff.

Oralabs, Inc. V. The Kind Group LLC and Eos Products

Oralabs filed a counterclaim asserted a counterclaim that their trade dress did not infringe on The Kind Group and Eos Products’ lip balm. In Oralabs Inc v. The Kind Group LLC and Eos Products, Twitter posts were introduced as evidence of confusion. While some of the posts introduced were not considered confusion as they contained awareness that the lip balm was similar, but not made by Eos, they did note that Miley Cyrus, a prominent social influencer, was amongst those confused which strengthened their claim.

Reverse Confusion

Wreal LLC V. Amazon.com, Inc

Wreal sells a streaming device known as FyreTV and claimed that Amazon’s Fire TV infringed on their trademarks. Wreal alleged reverse confusion in that consumers will mistake that Amazon, the junior user, is the source of the senior user’s product in the case of Wreal LLC v. Amazon.com Inc. The court ruled that tweets suggesting FyreTV had merged with Amazon’s Fire TV or the misuse of FyreTV when speaking about Amazon was not enough to show confusion. The court criticized the plaintiff for providing only a handful of examples amongst the thousands of inquiries related to Amazon’s Fire TV product.

Active Sports Lifestyle USA LLC, etc. v. Old Navy, LLC

Active Sports filed a complaint against Old Navy asserting reverse confusion over Old Navy’s sport line branded as “Active.” Active Sports was required to prove that the “active” name was not generic and that the national public had been exposed to their clothing in Active Sports Lifestyle USA LLC v. Old Navy LLC. Active presented online data including their website’s 20 million hits, their 87,500 followers on Facebook, as well as their substantial Twitter and Instagram followings. The court ruled given Active’s national media and social media exposure, that a jury could find that the strength of the mark favored Active.

Comparative Quality

GoSMILE Inc v Dr. Jonathan Levine

GoSmile relied on online reviews of their products and the defendants, Glo, to prove comparative quality in GoSmile Inc v. Dr. Jonathan Levine. The online reviews of Glo from the Home Shopping Network were compared to those of GoSmile from QVC, which included a comparison of the average star rating, number of customer reviews, and number of customer recommendations. When comparing the quality of a product, the court examines whether the defendant’s product is inferior and thereby tarnishing the plaintiff’s reputation if consumers are confused. However, a marked difference in quality reduces the likelihood of confusion. The online reviews were found to be admissible non-hearsay, but given their anonymous nature and small volume, the weight afforded to them was light.

Fame and Dilution

Chanel, Inc. v. Jerzy Makarczyk

Chanel opposed Jerzy Makarczyk from registering the term “Chanel” for his real estate development due to confusion and dilution in Chanel Inc v. Jerzy Makarczyk. In addition to other evidence presented, the court gave significant weight to Chanel’s social media presence. The court noted that Chanel’s Facebook page had 9.5 million fans and was ranked 5th amongst all fashion brands. They also considered the fashion icon’s Twitter, YouTube, Pinterest, Google+, Vimeo, and LinkedIn followings and rank compared to competitors. Due to Chanel’s high degree of unsolicited media attention and consumer recognition, Chanel was recognized as a brand synonymous with fashion and therefore considered famous for dilution purposes.

Bath & Body Works Brand Management V. Summit Entertainment

Bath and Body Works asked for declaratory judgement regarding their right to use the term “Twilight” on their personal care products from Summit Entertainment representing the movie, Twilight in Bath & Body Works Brand Management v. Summit Entertainment. Summit Entertainment submitted over 500 blog posts citing actual association of the two brands and weighed in favor of dilution by blurring. The court afforded these posts heavy weight.

Irreparable Harm

Life Alert Emergency Response v. Lifewatch

Life Alert Emergency Response claimed that Lifewatch was infringing on their trademark slogan, “help, I’ve fallen and I can’t get up.” Life Alert submitted consumer emails and social media posts complaining about robo-calls from what they assumed to be from Life Alert products, but were rather from Lifewatch products. In Life Alert Emergency Response v. Lifewatch, the posts were substantiated as proof of the threat towards Life Alert’s reputation and that irreparable harm was a result on the infringing slogan.

Key findings

Social media is becoming more relevant in today’s legal environment and appeal courts have approved of their inclusion into evidence, see Kibler v. Hall. It is important, however, to document filters and sampling techniques used to gather data and narrow down results to only relevant conversations. Testimony has been dismissed due to poor codification and lack of methodology in the collection and analysis of data, as seen in LVL XIII v. Louis Vuitton. Because social media captures the raw thoughts of consumers it can be ruled as non-hearsay based on excited utterance, present sense impression or existing state of mind. In You Fit v. Pleasanton, consumer thoughts captured in a Yelp review weighed in favor of likelihood of confusion.

Additionally, the comparative quality of a product can be used to argue for or against a likelihood of confusion. In GoSmile v. Levine, product reviews and star ratings were used to compare the products’ quality. A significant difference in product quality weighs against a likelihood of confusion. Furthermore, the number of social media followers and the extent of a brand’s social presence can lead to a finding of fame, as in Chanel v. Makarczyk.

Voluble’s Application

As seen in the summary table below, social media can be used in a variety of ways to help prove factors related to intellectual property and Lanham Act disputes. Through an understanding of when social media evidence has been found to weigh in favor of specific claims, Voluble can better tailor the methodology, presentation of data, and report meet the requirements of the court.

Table 1: Summary of case precedents including social media evidence

Key Findings of Social Media Evidence
Verifiability
Insufficient Evidence
LVL XIII Brands v. Louis Vuitton The expert testimony was excluded due to lack of methodology for collecting social media data and inability to replicate results.
Viking Yacht Company v. Composites One The experts had extensive experience within the industry, but due to a lack of methodology their testimonies were considered as expert opinions
Moroccanoil v. Marc Anthony Cosmetics The court overruled hearsay objection to social media posts, but agreed that the screenshots were not properly authenticated.
Secondary Meaning
Evidence of Claim
Paramount Farms v. Keenan Farm A large social media following lent credence towards the trade dress being considered famous.
Insufficient Evidence
Kreation Juicery v. Eiman Shekarchi Social Media photos including the trade dress were submitted, but the court ruled they were not proof enough to establish distinctiveness.
Penhurst Trading v. Zodax Social posts consisting of trademarked phrase in the form of a hashtag were submitted, but were not found to be exceptional
Predator International v. Gamo Outdoor Consumer comments on YouTube mentioning the trade dress were submitted, but were insufficient to prove secondary meaning alone.
Strength of Mark
Evidence of Claim
Kibler v. Hall The appellate court explained that proof of marketing and public awareness should have included data on social media following.
Likelihood of Confusion
Evidence of Claim  
You Fit v. Pleasanton Fitness Anonymous Yelp reviews were considered to be proof of potential consumer confusion.
Bulman v. 2bkco Twitter conversations coupled with articles of frustrated customers were sufficient evidence to weigh in favor on confusion.
Oralabs v. The Kind Group and Eos Products The court gave a higher weight to the social media evidence as they were posted by prominent social influencers.
Insufficient Evidence
Kind v. Clif Bar Court ruled that posts stating similarities were not indicative of confusion.
Reverse Confusion
Evidence of Claim
Active Sports v. Old Navy The strength of the mark favored the plaintiff due to their substantial Facebook, Twitter, and Instagram following.
Insufficient Evidence
Wreal v. Amazon.com The court criticized the plaintiff for providing only a handful of examples amongst the thousands of inquiries made.
Comparative Quality
Evidence of Claim
GoSmile v. Dr. Jonathan Levine Online reviews were found to be admissible non-hearsay, but weighed lightly due to their anonymous nature and small volume
Fame and Dilution
Evidence of Claim  
Chanel v. Jerzy Makarczyk The court recognized the top social rankings amongst industry competitors as evidence of fame for dilution purposes.
Bath & Body Works v. Summit Entertainment Over 500 blog posts citing association of the brands weighed in favor of dilution by blurring.
Irreparable Harm
Evidence of Claim  
Life Alert Emergency Response v. Lifewatch Consumer emails and social media complaints were proof of threat to plaintiff’s reputation and irreparable harm.

In regards to the authenticity and verifiability of social media evidence, tools such as Page Vault can help with the admissibility. Page Vault helps Voluble address Fed. R. Evid. Rule 901(b) which regulates the standard of authenticating evidence. Page Vault ensures that duplicates and printouts of electronic data accurately capture what is seen on the screen along with the metadata associated with the electronic post such as timestamps, URL, and IP address. Additionally, by enlisting a third-party provider, Voluble removes itself from the digital chain of custody and having to testify that the data has not been altered from its original form.

PART III: Brands and Consumers on Social Media

Overview

Voluble captures the naturally occurring, unstructured flow of online consumer conversations. In order to distill meaningful insights for litigation, Voluble needs a thorough understanding of the people and brands that are communicating on social platforms and how they are communicating. The conversation occurring across these major social media sites is representative of an extraordinarily large portion of the population. As of January 2017, the world population was 7.4 billion, of which 3.7 billion used the internet and 2.8 billion people were active social media users with 34% of people actively using social media on their mobile devices (Smartinsights, 2017). Facebook users generate over 1.5 million pieces of content daily and users send 350,000 tweets every minute (Lexalytics For Social Media, 2016).

According to a report by Statista, as of January 2017, Facebook remains the market leader of social media platforms. While social media users often use multiple accounts, some are more popular than others. It is estimated that Facebook has 1.8 billion active accounts worldwide and that it was the first social platform to surpass a billion active users. While Facebook was founded back in 2004, it has been able to maintain its dominance over newer platforms such as Twitter, founded in 2006, and Instagram, founded in 2009. At the start of 2017, Twitter estimated 317 million active users. Pinterest was the fastest of the social media platforms to 10 million unique users (Statista, 2017). The top ten social media platforms are listed below in Figure 6.

Figure 6: Social networks ranked by number of global users (Statista, January 2017)

While there are multiple social networks, each platform has a slightly different focus. Sites such as Facebook and Google+ are more focused on exchanges between family and friends and promote interaction through photo sharing and status updates. Other platforms such as Twitter and Tumblr are microblogs and emphasize rapid communication (Smart Insights, 2017).

Consumers Use of Social Media

Consumers use social media to gather news, stay current on industry trends, socialize with friends, as well as influence their purchasing decisions. According to the Global Web Index, the top reasons for social media use is to stay in touch with friends and their activities, stay knowledgeable about news and to fill spare time. These are all considered to be passive activities, which are not generating new content. For millennials specifically, social platforms have surpassed television as the main source of news (Deloitte, 2016). Only 39% of users are on these platforms to share their opinions and only 27% of social media users are using the platforms to share daily details about their lives (Global Web Index, 2014).

Today, the digitally-savvy consumer no longer needs to rely on the knowledge of the sales associate, but can turn to social media for trusted information from friends, family, product experts and personal reviews. A Deloitte study noted that nearly one in three U.S. consumers are influenced by social media in their purchases. According to the study, 85% of US consumers are currently using social platforms, of which 58% use them daily (Deloitte, 2015).

Brands Use of Social Media

Brands use social media to drive targeted traffic to further promote their brand. Having social media platforms, additionally boosts a website SEO. Not only are brands promoting themselves via social platforms, but they will also increase their ranking on search result pages (Google, 2017). In the 2016, The Social Media Industry Index produced by TrackMaven, 51 million posts were analyzed from 40,000 different companies across 130 industries to determine which social networks achieve the highest engagement per follower. Instagram dominates the average interaction per post, but Facebook leads over LinkedIn and Twitter. Because Twitter shares posts with a large audience, as opposed to more a targeted audience like Facebook and LinkedIn, companies must share more frequently to be heard which lowers their engagement ratio. In 2016, the average brands tweeted approximately 400 times a month, 225 posts on Facebook, 130 on Instagram and approximately 65 times on LinkedIn (TrackMaven, 2017).

Consumers and Brands

Social media can lead to relationship building amongst brands and consumers. It allows brands to interact with their customer base, by allowing them to read their consumer’s conversations, see their interests, and gain insights into their lives and hobbies. In return consumers interact with brands that they are interested in their product or service, interested in their promotions, and interact with brands that are entertaining as seen in Figure 7.

Figure 7: Leading reasons why social media users follow brands (Statista, July 2016)

Consumers are much more receptive to social media marketing as opposed to other forms of direct marketing. People view Twitter, Facebook and other platforms as social networks and less as marketing platforms (Social Media Examiner).

This allows companies to humanize their brands and provide more of a genuine person to person interaction and less of a business to consumer interaction. According to a survey conducted by Hubspot on 2017 marketing statistics and trends, 95% of millennials expect to be able to engage with brands via Facebook. On the other side, 42% of brand marketers say that Facebook is integral in their daily business (Hubspot, 2017). Approximately 60% of consumers expect brands to have a presence on Twitter as well. With Facebook users generating 1.5 million pieces of content daily, and 350,000 tweets a minute, brands can gain insights into consumers’ thoughts, hobbies, and interests (Social Media Examiner).

Brand Communities

Brand communities were first defined by Albert Muniz Jr. in 2001 in the Journal of Consumer of Research. He coined the term as “a specialized, non-geographically bound community, based on a structured set of social relations among admirers of a brand” (Muniz, 2001). Members of these communities are generally loyal and passionate about the brand and become ambassadors of it. Harley Davidson is an example of a company who has built a brand community of members who associate with the same lifestyle, activities and culture. While there are subgroups within the community, the community shares the same values.

While the community is built around a specific brand or product, a tribe focuses on the relationship between its members, the consumers. Bernard Cova stated in the European Journal of Marketing in 1996 that consumer tribes are composed of “groups of people emotionally connected by similar consumption values and usage, who use the social “linking value” of products and services to create a community and express identity.” A definition that still holds true today (Cova, 1996).  Nike has been able to build a tribe around high-performance athletes. Tribes can be global and create a sense of brand loyalty. The key to tribal marketing is providing a platform to interact with each other, which will simultaneously increase engagement.

Controlling Consumer Conversation

Because so many consumers are online, business reviews are important to the reputation of companies and their products and some companies have attempted to control the conversation. In a study conducted by Igniyte on the business of reviews, 79% of 500 surveyed business owners felt that their online reviews, comments and forum posts were important to the financial well-being of their business and the business reputation. 31% of those surveyed stated that being able to monitor and manage negative content is becoming increasingly important for their customer service and marketing departments. (Igniyte, 2015).

Consumer Review Fairness Act

Due to the business implications that fake reviews can have on the financial well-being, the Consumer Review Fairness Act was passed in 2016 in response to reports that some businesses were trying to prevent people from giving honest reviews about their products or services that they received. Companies were putting contract provisions within their online terms and conditions that allowed them to sue or penalize consumers for posting negative reviews. The Consumer Review Fairness Act protects a variety of honest consumer assessments, including online reviews, social media posts, uploaded photos, and videos. The act does not just cover product reviews, but it also applies to consumer evaluations of a company’s customer service (Federal Trade Commission, 2016).

Trolls and Unsolicited Harassment

Brands often must deal with trolls and unsolicited harassment as well. Trolls can be compared to prank calls or posts that bait brands. When brands fall victims to trolls and unsolicited harassment, brands can decide to block users from future interaction or request banning them from the social platform all together (Friedman, 2017). Blocking of a user will remove their ability to respond and reply to tweets in which genuine product and service conversations are occurring. Some negative content may refer to genuine customer-service or product issues posted from real consumers. These types of posts should not be removed and laws such as the Consumer Review Fairness Act help mitigate this.

Social Media Demographics

Social media was once reserved for the younger demographic, but has recently expanded to users of all ages. Most regular users engage in two to five accounts (Newberry, 2017). In 2005, only 5% of American adults were using at least one social networking platform. In just six years, American usage rose to 50% of all adults in 2011 and to 69% by the end of 2016. As more Americans continue to adopt social media, the usage of such platforms has expanded demographically as well (Cohen, 2017).

It is important to understand who is using social media and what platforms they are using. According to a study conducted by Pew Research Center, of adults who use at least one social media site, young adults, 18-29, are the most active users across all social media sites. Most people who are 30-49 are active users as well, but the usage rates decrease as the age brackets increase as can be seen in Figure 8. In 2006, a higher percentage of men than women used social sites as men were the early adopters. In 2009, the percentage of female users surpassed that of male and have continued to be the predominant users since. (Pew Research Center, 2016). In 2009, the subtle difference in demographics became apparent in terms of the percentage of adults engaging in social media. 2009 was the year that social media became mainstream and it was no longer considered to be just for millennials and social platforms saw their customer bases expand (Cohen, 2017).

Demographic Attribute Facebook Twitter Instagram Pinterest
All online adults 79% 24% 32% 31%
Men 75% 24% 26% 17%
Women 83% 24% 38% 45%
18-29 88% 36% 59% 36%
30-49 84% 23% 33% 34%
50-64 72% 21% 18% 28%
65+ 62% 10% 8% 16%
High school degree or less 77% 20% 27% 24%
Some college 82% 25% 37% 34%
College+ 79% 29% 33% 34%
Less than $30K/year 84% 23% 38% 30%
$30K-$49,999 80% 18% 32% 32%
$50K-$74,999 75% 28% 32% 31%
$75,000+ 77% 30% 31% 35%
Urban 81% 26% 39% 30%
Suburban 77% 24% 28% 34%
Rural 81% 24% 31% 25%

Figure 8: % of online adults using social platforms (Pew Research Center, 2016)

Facebook

While most regular users engage in multiple platforms, Facebook is the most widely used platform and therefore their demographics are similar to the all-encompassing social media user demographics. Facebook is used for social sharing with over 1.8 billion users worldwide and 1.3 billion mobile users. Users share approximately one million links every 20 minutes (Leverage, 2015). Facebook presents the largest opportunity of the social media platforms to communicate with consumers as approximately eight-in-ten adults online use Facebook. The 18-29, young adult age group use Facebook at the highest rate, but older adults continue to join the social platform. In 2016, 62% of online adults over the age of 65 are Facebook users, which increase from only 48% the year prior (Pew Research Center, 2016).

Twitter

Twitter is a microblogging site and has the largest infiltration in the United States with 21% of all U.S. adult internet users on Twitter. As of 2015, users tweeted approximately 9,100 tweets every second (Leverage, 2015). For Twitter, there is a decrease amongst all age categories compared to Facebook, as Twitter has less users. Younger adults, 18-29, are more likely than those 65+ to be active Twitter members, with over three times the percentage of users. Twitter is also a more popular platform for the highly-educated user, capturing 29% of the demographic, compared to only 20% of those with high school degrees or less (Pew Research Center, 2016).

Instagram

Instagram is used as a photo sharing site with over 300 million active users. Brands are participating using hashtags and posting images that consumers can relate with. The most followed brand on Instagram is Nike with over 70 million followers (Leverage, 2015). Approximately one-third of adults who are online report to be users of Instagram. Compared to the other social platforms included in the survey by the Pew Research Center, approximately six-in-ten young adults use Instagram, which is nearly double that of the 30 to 49-year-old bracket, and seven times that of the 65+ age group. Additionally, female, online adults are more likely to use Instagram with a usage rate of 36%, compared to male users at 26% (Pew Research Center, 2016).

Pinterest

Pinterest is a social site that is based on discovering new products for a DIY consumer base of 70 million users (Leverage, 2015). The Pinterest usage rate by women doubles that of men at 45% compared to 17%. Over a third of online adults 18-49 use Pinterest, but only 16% of adults over the age 65 claim to be members (Pew Research Center, 2016).

Advertising Trends

Previously, for consumers to contact a company, consumers were required to communicate via phone, email, or the company website. Today, consumers are given the additional option of communicating via social channels. According to a survey conducted by Hubspot on the social lifecycle, 95% of millennials expect to be able to engage with brands via Facebook. On the other side, 42% of brand marketers say that Facebook is integral in their daily business. Approximately 60% of consumers expect brands to have a presence on Twitter as well (Hubspot, 2014).

Digital Marketing

According to Jamie Turner, an award-winning author and digital marketing expert, digital advertising has become a popular means of advertising over more traditional advertising methods, such as TV and print. One reason for the increase in popularity of digital marketing and specifically social media marketing is that the effectiveness of the campaign can be measured. Brands can measure fan engagement by analyzing their interaction in terms of likes, retweets, and shares. Additionally, website referral traffic can be measured to compare how much traffic is being driven from social media platforms compared to other marketing efforts. Most importantly for most brands, is the number of conversions, whether that be a sale or a download, or subscription etc. (Turner, 2015).  While consumer engagement and traffic are beneficial, a successful campaign will ultimately be measured against its conversion rate. Ultimately, the top benefits of social media marketing are to increase exposure and traffic (Social Media Examiner).

A study from the Journal of Marketing Research found that Facebook likes alone do not translate into increased sales, but consumers also need to be targeted with paid advertising. Social media advertising budgets have doubled in the past few years from $16 billion in 2014 to over $30 billion in 2016 (Journal of AMA, 2016). Facebook’s total social ad revenue was over $6.8 billion in the 3rd quarter of 2016 compared to Twitter who brought in $545 million during that same period. Snapchat sold $367 million in social ads throughout 2016. Overall in 2016, social ad spending was $32.97 billion (Newberry, 2017).

A survey by Forrester Group was conducted on the reach of the top 50 global brands on Twitter. The survey revealed that these brands reached approximately 3.61% of their followers with a tweet, known as an impression (Forrester,2015).  A brand such as Forbes with approximately 7.8 million followers, therefore reaches approximately 281,000 of its followers per tweet. For Facebook, the average reach for posts decreases as the number of Facebook likes increase and this is due to disingenuous followers.  According to a study by WebpageFX, each tweet on Twitter collects impressions for only 24 minutes, Facebook posts receive 90 minutes of visibility, but pins on Pinterest can last 151,200 minutes Due to the prolonged organic reach, 93 of the top 100 brands have accounts on the Pinterest platform (Kohler, 2015).

In a study conducted by Locowise, who specializes in social media analytics for marketing agencies, the average reach for posts decreases as Facebook likes increase. Facebook pages with over a million likes have an average of 2.27% organic reach (Cohen, 2015).  A company like Forbes with 4.4 million followers would reach 96,800. Instagram has an organic reach of 20%, Instagram has the highest proportion of content going viral with over 250 interactions. Only 10% of all Instagram photos and 6% of videos receive less than 10 interactions (Burney, 2017). While Forbes only has 1.4 million followers on Instagram, their reach in nearly that of Twitter.

Promoted Tweets

Promoted Tweets are no different than ordinary tweets but they are purchased by advertisers who are looking to reach a wide group of users or increase engagement from their current followers. Besides the “Promoted Tweet” label, promoted tweets can be retweeted, replied to, and liked as regular tweets do. Promoted tweets can be seen in search results, timelines, and user profiles (Twitter Business, 2017). These promoted tweets are targeted and only appear to those who are likely to be interested and is relevant to that user. This is determined by how users interact with certain tweets, what they retweet and who they follow.

Facebook Ads

Facebook ads can be used to raise awareness, drive demand, and boost sales. Facebook allows for targeted ads based on user demographics, behaviors and contact information (Facebook, 2017). In November of 2014, Facebook no longer allowed for brands to post overly promotional page posts. Previously, these posts would contribute to the brands organic reach, but now it is considered part of a brand’s sponsored content (Facebook, 2017). Facebook ads can be tailored to target users based on demographics, behaviors, and contact information. Their easy to use interface and ad reporting tools make Facebook ads accessible to the everyday marketer.

Fake Accounts and Bots

Overview

Social media platforms are frequented by millions of individuals, which makes the presence of bots enticing (ACM, 2017).  Bots are computer algorithms which are designed to exhibit human-like behavior. Social bots automatically generate content and interact with users on social media networks. Bots can be used in a variety of different ways. Some are designed to aggregate content, respond to inquiries, or provide information.

There is a massive number of fake accounts both of which are active and dormant on various platforms. These types of accounts can be used to fake the number of followers, send spam and boost interest in trending topics by increasing circulation. Users can pay to have bots follow their account or dilute posts about controversial topics. According to Lotan, the chief data scientist at Betaworks, these bots are easily commissioned with the going rate of $5 for 4,000 followers (Lotan, 2014). These fake accounts do not act the same as more traditional bots in terms of automating posts. These fake accounts are often interconnected to make networks of fake accounts which work together to rapidly boost followers or trending topics (Finger, 2015). Users can pay to have bots follow their account or dilute posts about controversial topics. These fake accounts do not act the same as more traditional bots in terms of producing automating posts. These fake accounts are often interconnected to make networks (BBC, 2017). It is estimated that Facebook has approximately 170 million fake accounts, Twitter has 48 million and Instagram has 45 million (Newberg, 2017). While networks have been trying to remove these accounts, new technologies are allowing accounts to be made at a rapid pace and the networks can’t keep up. Bots are a type of fake account that can either be helpful or corrupt.

Uses for Bots

Bots can be created to exploit and deceive. Bots can artificially inflate the following and support of certain products, companies, or people, while smearing competitors with fake news reports (Haustein et al., 2015). The main issue with bots is that they give a false impression that certain information is highly endorsed, regardless of its accuracy. False stories can be quickly circulated due to bots retweeting posts without fact checking and verifying sources. Some of the more sophisticated bots can appear as credible followers and are more difficult for users and algorithms to detect (Haustein et al., 2017).

As artificial intelligence interconnects our society, bots can exploit the use of social media. A recent bot campaign created a sustained discussion on social media platforms about Cynk, a tech company. Automatic trading algorithms detected the spike in conversations and began to trade heavily in the company’s stock. The market value for Cynk increased 200-fold to a worth of $5 billion. By the time analysts realized the deceit of the bots, heavy losses had already been incurred (Ferrara et al., 2016).

While bots can certainly be deceitful, companies are also known to use bots to improve customer service. Brands have begun using bots to automatically respond to inquiries for customer care. These types of bots are designed to be helpful by answering frequently asked questions in a timely manner. Bots can join discussions by identifying relevant keywords and searching the internet for information on the topic. Through natural language algorithms, bots can produce responses and even include references to external sources (Ferrara et al., 2016).

Deciphering Bots from Humans

While bots are progressing and deciphering the differences between humans and bots is becoming more complex, there are subtle differences that can help. Academic research published by the Association of Computing Machinery, has discovered that bots often retweet more than human users and have longer usernames and younger accounts, whereas humans receive more replies, mentions and retweets. In addition to bots, there are also cyborgs and are very common on Twitter. Cyborgs are human-assisted bots that post tweets during the absence of the users. While bots are 100% automated, cyborgs interlace both manual human posts and automated posts. In a study conducted by Chu, Gianvecchio, Wang, and Jajodia (2012), it was discovered that 10.5% of all Twitter accounts are bots, with an additional 36.2% of accounts being categorized as cyborgs.

The Institute of Electrical and Electronic Engineers issued a study which analyzed twitter conversations in order to classify users as humans, bots, or cyborgs. Humans typically use Twitter as a micro-blog in which they share what they are doing or how they are feeling with intelligence and originality. Bots lack intelligence and original content, as seen by retweeting, automated updates, and duplicate tweets. For bots to gain human attention, they need to be in front of a large audience. Bots do mass followings within small windows of time with the goal of being followed back. Twitter imposed a limit on the ratio of following to followers to suppress bots. Due to this, more sophisticated bots now unfollow those who do not follow them back and therefore keep their ratio close to one.  Human celebrities tend to have many followers, but follow very few people themselves. Therefore, their account reputation is close to one. On the other side of the spectrum are typically bots who have few followers, but follow many and have a reputation close to zero (Chu et al., 2012).

Cyborgs tend to post the most number of tweets. A large percentage of these accounts are created by companies as a media and customer service channel. These services are typically tweeted using automated tools such as RSS widgets. Additionally, these sites tend to be maintained by company employees who also communicate with customers and generate their own content. While bots are known to tweet more frequently in their active period, they are often suspended for extreme activity. Due to the suspension and hibernation periods of bots, humans tweet more. Bots however, have high frequency within a short amount of time, whereas humans tend to tweet more sporadically over a longer period (Chu et al., 2012).

Most bots tend to include URLs in tweets to redirect visitors to external webpages, like spam e-mails. Due to the character limit imposed by Twitter, spam tweets typically contain an appealing title and an external URL. As previously mentioned, human tweets typically contain opinions and feelings and therefore do not require external URLs. Only 29% of human tweets contain URLs, whereas 97% of bot tweets contain URLs. The majority of spam content is posted by bots, with very little coming from humans (Ferrara et al., 2016).

Influencer Scores

Klout Scores

Influencer scores were designed to find influential people in niche topics. Klout scores measure a person’s influence about certain topics based on social network data. Klout scores are measured on a scale of 1-100 and are based on public interactions, who you follow, who follows you, hashtags used, connection, retweets, and topics that we engage with. When you share something on social media that people respond to, your influence increases and your Klout score increases. According to Klout’s commercial website, it is better to have a small and engaged audience than a large network that doesn’t respond to the content.

As presented at the International Conference of Big Data in 2015, there are a variety of signals that determine one’s Klout score. These signals include the ratio of reactions generated compared to the amount of content shared. Additionally, value is generated by engaging with a variety of unique individuals as opposed to a select few. Overall, Klout measures over 45 billion interactions every day (Big Data, 2015). Klout scores provide a straightforward way to evaluate a user’s social influence, but it provides little insight into the components that made up the score. Other social media tools offer reach, engagement, retweets.

Other Measures of Influence

Other measures of influence include PeerIndex and Kred. PeerIndex evaluates social influence similar to that of Klout. PeerIndex produces an influence score based on activity level, user engagement and how influential their following is. This scoring system allows users to find relevant social media influencers in their industry (PeerIndex). PeerIndex is used by social analytic platforms such as Brandwatch and Hootsuite. Kred scores are based on influence and outreach. Influence is measured based on retweets, replies, and mentions to you, while outreach relies on the same measurements but by you. Kred scores can be broken down geographically to see where their influence is the greatest (Kred).

Industry Benchmarks

When comparing a brands presence on social media, it is important to consider how other brands are preforming within the industry and each social platform that they are publishing on. The Social Media Industry Index produced yearly by TrackMaven, analyzes 40,000 companies that encompasses 130 major industries. According to the data for 2016, the top industries on Facebook by follower count include animation, entertainment, and broadcast media whose average followers ranged from five to six million. Apparel and fashion brands ranked 8th, with an average following of 2.5 million and sporting goods ranked 10th with an average following of approximately 2.3 million as seen in Figure 9.

  Facebook Twitter Instagram
Consumer Goods
Average Followers 530,852 57,364 330,084
Average Posts Per Month 41 82 55
Average Interaction Per Post 1,501 43 2,828
Retail
Average Followers 1,708 185,417 421,660
Average Posts Per Month 139 270 149
Average Interaction Per Post 2,669 79 5,408
Apparel & Fashion
Average Followers 2,508,627 566,800 173,480
Average Posts Per Month 154 200 146
Average Interaction Per Post 830 154 14,866
Sporting Goods
Average Followers 2,290,325 445,064 238,034
Average Posts Per Month 94 237 116
Average Interaction Per Post 895 350 30,558

Figure 9: Average social media metrics by industry (TrackMaven, 2016)

As seen in the data, Twitter is largely dominated by live news and businesses within the media industry. The broadcast media industry has the highest average following of over 3 million followers, which is followed by newspapers at 2 million and music at 1.4 million for the average follower. In terms of engagement, the music industry has the highest engagement rate with 1,745 retweets or replies per post. With Instagram, the top industries by average follower count are entertainment, hospitality, and music ranging from approximately 950,000 followers to 765,000 followers. The sports industry ranks sixth with approximately 500,000 followers and retail brands are tenth with an average following of 400,000 (TrackMaven, 2016).

Additionally, Statista ranks the top followed accounts for each social platform. On Facebook, the top brands are Facebook for Every Phone with over 498 million followers, followed by Coca-Cola with 102.8 million followers, then YouTube, McDonalds and Red Bull as seen in Figure 10.

Figure 10: Product Brands by Facebook followers in millions (Statista, February 2017)

On Twitter the most followed brand is Youtube with 66.17 million followers, followed by Twitter with 58.69 million, followed by CNN Breaking News and Instagram (Statista, 2017) as seen in Figure 11 below. In terms of top consumer brands, PlayStation has the largest following of 13.8 million, followed by Chanel with 12.8 million and Samsung Mobile with 12.1 million followers.

Figure 11: Brands on Twitter by followers in millions (Statista, January 2017)

As of February 2017, the top leading global brand on Instagram was Nike with 69.4 million followers, followed closely by National Geographic with 69.2 million followers and then Victoria’s Secret with 51.3 million as seen in Figure 12 (Statista, 2017).

Figure 12: Brands with the most followers on Instagram in millions (Statista, January 2017)

Voluble’s Application

Prior to any data collection, Voluble conducts background research on the relevant brands, competitors and industry. This background research informs the development of queries that define the data for each case. The queries are constructed to capture as much of the relevant social media conversation as possible while filtering out irrelevant posts. To ensure that Voluble has captured the optimal dataset for analysis, queries are continually refined as posts are reviewed and an understanding of what consumers are say, where they are saying it and how they are saying it is developed as seen in the figure below.

Figure 13: Voluble’s use of social media data

Using custom algorithms and metrics designed for each case, the universe of potentially relevant posts is cleaned to eliminate irrelevant and inorganic posts. The remaining conversations can be used to perform quantitative analyses. For example, Voluble can determine how often consumers talk about at-issue trademarks, compare volume of online conversations across multiple brands and dates, and assess the extent to which consumers mention the plaintiff’s and defendant’s brands together. These conversation can be also be used as anecdotal evidence to demonstrate the affinity and awareness amongst consumers of the mark, to provide examples of consumers who are confused by infringing marks, to demonstrate consumer sentiment surrounding the mark, and to provide evidence supporting other claims as seen in the table below.

Figure 14: Social media exemplars supporting litigation claims

Key Findings

There are a massive number of fake accounts and bots, both active and dormant, on various social platforms.  Bots can artificially inflate the following and support of certain products, companies, or people, while smearing competitors with fake news reports. Therefore, Voluble needs to be able to decipher between bot and human generated posts. Voluble has developed tools that enable it to identify many of these posts by relying on subtle differences between human and bot behavior, such as the higher volume of activity, excessive use of characters, unoriginal content, and use of external links that characterize posts by bots. Through background research and text analysis, false positives can also be removed. These conditional scripts allow for the data to be cleaned more efficiently.

Benchmarking the data allows for further insights into brands competitiveness, level of engagement and reach across the industry. These benchmarks that will prove useful to Voluble as it is important to understand how direct competitors are preforming within the same industry. Each industry varies in terms of social following and performance metrics. Voluble reports will be written within the context of the industry. Sites such as SocialBakers provide statistics for top social media accounts by industry sectors as well as geographically. Additionally, data can be analyzed on a platform-by-platform basis and compared to what consumers are saying on each platform to determine if there are fundamental differences across platforms. Every platform represents a unique population, but Voluble’s audience-specific analysis keeps these demographics in mind.

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