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  • 10 Jun 2017 10:20 AM | Kathy Doering (Administrator)


    Social Media Analytics Market: Global Size, Share, Trends & Forecast, 2016–2024

    Social Media Analytics Market: Global Size, Share, Trends & Forecast, 2016–2024

    NEWS  June 8, 2017, by shawn  52

    Global Social Media Analytics Market: Overview

    The process of collecting, analyzing, and reporting on the data that are available on various social media is known as social media analytics. It can be used as an efficient market research tool for business purposes in order to enhance the effectiveness of a website. Social media monitoring helps to view brands on various platforms of the social media. It will analyze the impact of campaigns, assess competitor activity and emerging trends, and identifies opportunities for the engagement. It enables systematic identification, data analysis, and extracts social media data by using sophisticated tools and techniques. Social media analytics have become one of the important tools in the enterprises as it helps in implementing short-term plans and strategies. The social media analytics are used in various applications such as marketing management, customer segmentation & targeting, multichannel campaign management, customer behavioral analysis, and competitor benchmarking.

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    Global Social Media Analytics Market: Growth Factors

    The emergence of the advanced analytics techniques and business intelligence coupled with an immense upsurge in the number of social media users is expected to drive the global social media analytics market growth. Massive investments made in the social media analytics and increasing focus on the competitive intelligence contributes to the overall market growth. Increasing adoption of the social media analytics and cloud computing positively enhances the global market. Increasing need for targeting and customer segmentation among SMEs and large enterprises in order to build brand planning and marketing strategies anticipates fueling the market growth. The major factor responsible for the market growth includes growth in the number of social media users and improving business strategies & competitive intelligence. Conversely, increasing complexities associated with the analytical workflow and lack of skilled expertise hinders the social media analytics market to some extent.

    Global Social Media Analytics Market: Segmentation

    The global social media analytics market is segregated based on the end user, application, and geography. The application segment is segmented into multichannel campaign management, marketing management, customer segmentation & targeting, customer behavioral analysis, and competitor benchmarking. On the basis of end-user, the global market is bifurcated into travel & hospitality, media & entertainment, retail, BFSI, IT & telecom, healthcare, and others. Geographically, the global social media analytics market is diversified into North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa.

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    Global Social Media Analytics Market: Regional Analysis

    North America contributed to the major market share in the global social media analytics market due to rising demand for social media analytics. Europe is significantly adopting social media analytics owing to the presence of advanced telecommunication network. Asia-Pacific region is expected to grow at fastest rate in the global social media analytics market. The Middle East and Africa region shows a massive surge in social media and has increasing need to examine data and generate valuable insights which anticipate propelling the market growth.

    Global Social Media Analytics Market: Competitive Players

    Major leading players in the global social media analytics market include Tableau Software, SAP SE, Netbase Solutions, Inc., Salesforce.com Inc., Adobe Systems, Inc., SAS Institute, Hootsuite Media, Inc., Crimson Hexagon, and IBM Corporation.

    Browse detail report @ https://www.zionmarketresearch.com/report/social-media-analytics-market

    About Us:
    Zion Market Research is an obligated company. We create futuristic, cutting edge, informative reports ranging from industry reports, company reports to country reports. We provide our clients not only with market statistics unveiled by avowed private publishers and public organizations but also with vogue and newest industry reports along with pre-eminent and niche company profiles. Our database of market research reports comprises a wide variety of reports from cardinal industries. Our database is been updated constantly in order to fulfill our clients with prompt and direct online access to our database. Keeping in mind the client’s needs, we have included expert insights on global industries, products, and market trends in this database. Last but not the least, we make it our duty to ensure the success of clients connected to us—after all—if you do well, a little of the light shines on us.



  • 9 Jun 2017 8:51 AM | Mark Michelson

    Sweta Patel  | June 7, 2017 | As published on MarketingLand

    Have you been looking for the best way to measure the effectiveness of each of your social media channels? Do you want to learn how you can pick an effective social media attribution model for your business?

    In this article, I’ll describe the three different social media attribution models and how each one uniquely measures the success of your social channels. You can decide which approach will work best for your particular situation.

    The single-touch model

    The days of referring to social media efforts as strict awareness marketing are long gone. Between promotional posts, blatant CTAs and paid advertising, businesses use their social channels to drive leads and purchases every day. So how do we measure the effectiveness of each channel and effort?

    The single-touch social model is based on two types of touches. The first-touch model focuses on the first channel, also known as the discovery channel. No matter where your prospect converts, this model gives all of the credit to the very first instance/place where your prospect found you.

    For example, as I was browsing my Instagram, I came across an ad that pertained to comfortable shoes. At the time, I was working the booth at an event and my heels were killing my feet! In that moment, I was convinced to consider these shoes. I then researched them on the web and scrolled through their Twitter feed, but I didn’t buy them just yet.

    Later, I saw the same shoes on a Facebook ad, and I purchased them. According to the single-touch model, the initial interaction on Instagram would receive all the credit for the shoe purchase or conversion. This is the most common model, but it is also the least accurate.

    First, I went to Instagram and saw the ad:

    Then, I went to Twitter to research their newsfeed:

    Afterward, I went to their website to see what they were about:

    Last, I viewed my Facebook feed, where I came across their Facebook ad:

    If I were focusing on a last-touch social model, then all my credit would be given to the Facebook ad that I clicked on to make my purchase. Essentially, this is the last-touch social model. All the credit is given to the last touch point before the conversion happens.

    There are tools like Google 360 and Bizible that enable you to measure attribution at any instance, and Google plans to release a free version called Google Attribution. These tools gather data from across multiple touch points in today’s complicated string of customer journeys so that you can view metrics in a simplified way. It’s this information that allows marketers to make smarter decisions about where to focus time, effort and budget across social media.

    Optimize your channel marketing with multi-touch model

    Unfortunately, single-touch models can be misleading, as we are not sure which touch points were the most effective or convincing. Today, vanity metrics like those provided from single-touch models don’t make the cut. We must dig deeper to understand our metrics and where we need to focus both attention and budget when it comes to social media.

    This is where the multi-touch social model comes in handy. It allows you to measure the effectiveness of each and every touch point that leads to the ultimate conversion.

    Every touch point is equally weighted in this model. This means each point of contact received the same amount of credit as any other. If I refer to the previous example with the shoes, then all four touch points, from the Instagram news feed to the Facebook ad, would receive the same credit for the closed purchase.

    Marketers primarily use the social multi-touch model to understand prospect behavior around the conversion. Which social media channels should they increase their focus on? Which channels should they completely eliminate because they are not influencing conversions?

    The multi-touch social model is more precise than the other models because it allows you to measure the entire customer path to purchase. Some marketers may feel that not all touch points are equal, so they look for more of a position-based social model.

    Generate more revenue with a position-based model

    The position-based social model focuses on giving two touch points on the path to purchase more weight than the other two touch points. You can choose which points have more weight. I’ve seen that most marketers choose to give a higher percentage of weight to the outer points of the model rather than the two inner points of the model.

    For example, you can attribute 40 percent of the weight to the first and the last touches of the model. In the Time Slippers shoe example above, we would give 40 percent of the weight to the Instagram news feed and another 40 percent to the Facebook ad.

    Then we would attribute 10 percent to the website and 10 percent to the Twitter feed. You can easily move these percentages around based on how your prospects make a purchase.

    This social attribution model may lead to different biases based on the impact of your channels. However, if you want to report on your metrics for social media, this approach is more effective than taking a vanity metric approach. Vanity metrics give you a great overall picture of reach and popularity, but they do not connect your channels to revenue.

    Conclusion

    Social attribution models offer the best options for measuring real social media ROI based on the activity of your prospects. Most of the time, I take a multi-touch social attribution model approach because it helps me understand the customer path, rather than laser-focusing on a couple of touch points that may or may have not been key indicators.

    There is no right or wrong way to measure social interactions, but we can certainly aspire to the highest accuracy possible.


  • 7 Jun 2017 1:18 PM | Kathy Doering (Administrator)

    Posted October 28, 2016, DigitalMR


    Challenge

    In 2014, a renowned maker of luxury wrist watches commissioned DigitalMR to harvest and analyse social media data related to their product category, in order to understand what people were saying about their brand and discover some actionable business insights.

    A brand in an exclusive product category such as luxury watches is not expected to have a huge volume of posts or a large share of negatives, so it can mainly benefit from analysis that is granular enough to extract all the precious insights that a relatively small dataset can offer.

    Solution

    41,794 posts were harvested using the brand names. This was reduced to 12,520 after round 1 of noise elimination, to 4,423 after removing commercial (i.e. not consumer) posts, and finally to 4,134 after round 2 of noise elimination.

    In order to discover conversation drivers we created a hierarchical taxonomy of topics for luxury watches customised to the reporting needs of the client. The taxonomy was enhanced by topics which emerged through what people were posting online. This process in addition to showing sentiment by topic and sub-topic within brand, also enabled us to provide our client with Brand Share of Voice and Net Sentiment Score© benchmarking (NSS = a DigitalMR metric which takes into account the no. of positive and negative posts) for all topics and subtopics.

    Further to that, through our unique process we were able to identify ~513,000 posts from people contemplating buying a watch. This enabled the client to directly come in contact with social media users who could become customers. We also found influencers with a large following discussing watch brands in thousands of online posts, not only on social media, but also on blogs and specialized forums, so that the client could reach out to them and explore co-operation.

    Result

    This helped the client gain a better understanding of the social landscape around their product category and discover what consumers are saying with high sentiment accuracy. It also gave them the opportunity to engage with consumers behind posts expressing purchase intent, as well as category influencers which could potentially collaborate with the brand.


  • 4 Jun 2017 7:14 PM | Kathy Doering (Administrator)

    Students at private, expensive universities exhibit better mental health than public school peers

    By Jason Maderer | JUNE 1, 2017 • ATLANTA, GA

    Using information gleaned from social media, researchers from the Georgia Institute of Technology have created a mental health index for the nation’s highest-ranked colleges and universities. Their study looked at five years of data on Reddit, scanning it for comments about issues that included depression, financial and academic anxiety and thoughts of suicide. Schools were given a score based on the frequency of those threads and robustness of the conversations.

    The research found that students at higher-ranked schools have better mental health than those at lower-ranked colleges. The well-being index is also better at universities with higher tuition. It’s lower at large public schools with a majority of female students.

    The study doesn’t identify schools individually for privacy reasons.

    “Online conversations about mental health issues are definitely increasing,” said Munmun De Choudhury, Georgia Tech’s assistant professor, from the School of Interactive Computing, who led the study. “We saw it rise 16 percent from 2011 to 2015, even after we took into account that Reddit has become more popular in recent years.”

    The study focused on the top 150 universities as ranked by U.S. News & World Report, gathering data from the 109 of them with active Reddit accounts.

    De Choudhury suggests that the reason there are less posts about tuition anxiety at more expensive colleges is because their students tend to be more affluent than the average public school attendee. Therefore, the stresses of paying for college or going into debt aren’t as great. De Choudhury also thinks that the wider variety of backgrounds at large public schools likely lends itself to a more robust sharing of stress and anxiety issues.

    “As for more posts about mental health at universities with more females, I don’t think it’s necessarily because women have more anxiety,” De Choudhury said. “Research has traditionally shown that females are more likely to express their emotions and feelings whether in an offline setting or social media. It’s not that they have poorer mental health — it’s that they’re more likely to talk about their troubles while online.”

    The study also found an overall increase in mental health posts as the academic year progressed. It peaked in the fall in November and was even higher in May. It gradually decreased each summer.

    The Georgia Tech team used a method called transfer learning to mine the data. First, they created a model to scan subreddit threads about depression, bipolar disorder and other issues to learn patterns of what is written when people discuss mental health. Then the model was used on the higher education subreddit communities.

    The algorithm scanned the five years of discussions, day by day, looking for clues. Once it found keywords and phrases, it measured the breadth of the conversations. The more frequent the posts and depths of the discussions, the lower the respective mental health indexes.

    Overall, three percent of the threads on the 109 sites were about mental health topics.

    De Choudhury has previously done similar social media research to help identify expressions of eating disorders, postpartum depression and suicide.

    “I’ve always wondered if there are things about college students and mental health that we could learn by using these same social media research tactics,” De Choudhury said. “We wanted to see if we could do something more collectively at the campus level before digging deeper to understand the challenges individuals might be facing. Perhaps this data can be used to help campus administrators as they create policies in real-time to benefit student bodies dealing with stress and anxieties.”

    The paper, “A Social Media-Based Index of Mental Well-Being in College Campuses,” was presented this month at the 2017 ACM Conference on Human Factors in Computing Systems (CHI 2017) in Denver.


  • 2 Jun 2017 1:49 PM | Kathy Doering (Administrator)

    It turns out mining social media for research and insights is not just for the consumer goods industry. More and more researchers are mining social media and online content for disease control. Recently Johns Hopkins conducted a study that revealed online data has several uses. Concentrating on those diseases that spread quickly such as the flu or influenza have been the primary focus. The data mining shows strong potential for tracking other diseases as well.

    In order to create relative information it is important to create media filters to scrape the data first. Then one can look at the analytics to identify geographic trends and severity of an illness or disease. Are certain age groups being affected more than others? Which area of the country is showing higher outbreaks? This along with other data can help researchers in alerting the public.

    While attending The Social Media Shake Up in Atlanta last week, one software provider discussed how they have been tracking medications and their affects on patients through social media mining. Additionally, Pharma is able to find "influencers" who have had success with their products and further engage with them to learn more. As the image below identifies, tracking where the heaviest chatter is occurring can be very beneficial when determining marketing spend and educational opportunities.

    The American Medical Association has published a great article that discusses the use of social media in the medical industry.

    While we must move forward with caution and ensure that a patient's privacy is the priority, I do think that this kind of information can be added to the other research methods used by the industry. Especially since it has been noted that 80% of people will research a medical problem or illness on the internet.


  • 1 Jun 2017 2:41 PM | Mark Michelson

    By Ronan Shields | 31 May 2017 | As published in The Drum

    View the full deck here: https://smra-global.org/Downloads

    The latest Internet Trends report from KPCB is out, suggesting flat internet user growth, slowing smartphone shipments, increased ad spend on mobile, with 'the dupopoly' capturing the majority of this. 

    The report, ‘aka The Mary Meeker report’ is often touted as a ‘must read’ by leading minds in the media industry and was unveiled today (May 31) by the Kleiner Perkins Caufield Byers analyst at Recode's Code conference

    Coming in at well over 300 slides long, the comprehensive study spans a vast range of topics from user numbers, media trends, gaming, and even healthcare. The Drum selected the top line figures concerning media professionals. 

    • The global number of internet users in 2016 was 3.4 billion with annual growth flat at 10%

    Kleiner Perkins

    • Smartphone shipments are increasing (total users just shy of 3 billion) but the rate of growth continues to slow 

    Kleiner Perkins

    • In the USA the average adult spent 5.6 hours per day consuming media online 

    Kleiner Perkins

    • And advertisers continue to move their budgets online with digital ad spend totaling $73bn 

    Kleiner Perkins

    • Although ad spend on mobile devices continues to trail consumer media consumption

    Kleiner Perkins

    • This has been accompanied by Facebook and Google’s increased share of ad spend, their respective growth rate significantly outpaced the market

    Kleiner Perkins

    • Increased digital ad spend has been accompanied by pressure on marketers to show that it produces results, but proving ROI is a challenge

    Kleiner Perkins

    • Adblocking is on the rise, especially in emerging economies where internet consumption is primarily via cellular networks 

    Kleiner Perkins

    • Improvements in technology are making it easier for marketers to attribute offline purchases to online ad spend 

    Kleiner Perkins

    • The financial value of mergers and acquisitions in the tech sector during 2016 ($336bn) was down year-over-year but the number of deals was up 

    Kleiner Perkins

    A full copy the report, which collates findings from a number of different data sources, can be downloaded here

  • 1 Jun 2017 12:56 PM | Mark Michelson

    By Mary Meeker Partner at Kleiner Perkins Caufield & Byers

    Published on May 31, 2017 

    Featured in: Editor's PicksEntrepreneurshipMobileTechnologyVC & Private Equity

    Click here for full deck

    Focus Topics: 

    • Online Ads and Commerce: More Measurable and Actionable: A review of current advertising trends shows an increased focus on the measurability of online ads. Also: the interconnectedness of ads and commerce where ads are the storefront. (Slides 10-79) 
    • Interactive Games: Evolving, Pervasive, Driving Innovation: Esports have become one of the world’s biggest spectator sports, Also, there’s growing use of games for education and learning, providing skills that are hard to obtain otherwise. Gaming is a leading indicator for many elements of today’s consumer/enterprise tech landscape. (Slides 80-150) 
    • Media: Distribution Disruption: Digital streaming businesses have changed the media game through scale and personalization. Consumers are increasingly wary of the bundle, opting for the choice, personalization and pricing of both large and emerging subscription media businesses. (Slides 151-177) 
    • Healthcare: At A Digital Inflection Point: Digitization of patient data, pharmaceutical testing and medical records is driving faster and more accurate insights. (Slides 288-319) 
    • Enterprise: Clouds on the Horizon: Widespread adoption of cloud computing is driving a revolution in enterprise applications, but also a corresponding rise in security threats. (Slides 178-192) 
    • China: Golden Age of Entertainment and Transportation: Increasing mobile user engagement in China is catalyzing an evolution in mobile-centric entertainment and financial technology. Also, China is the leading on-demand transportation economy globally – as bike-sharing companies rapidly emerge. (Slides 193-231) 
    • India: Competition Continues to Intensify…Consumers Winning: Behind China, but leapfrogging to new technology infrastructure driven by Prime Minister Modi's policies and 1.2 billion people with digital identity profiles. (Slides 232-287) 

    The deck covers a broad array of topics, including global internet user trends, advertising and e-commerce, gaming, online media, digital health, and much, much more. This guide is intended to highlight some of the key topics of discussion in this year’s edition.

  • 1 Jun 2017 12:19 PM | Mark Michelson

    by Laura Forer  |  June 1, 2017  | As published on MarketingProfs.com

    Remember when we surfed the Internet on a computer or laptop while seated at a desk?

    It wasn't that long ago, but times have changed. Now we consume content wherever we are, whether that's at home or at work or en route to a store. And our gadgets have changed from stationary computers to myriad mobile devices that we carry or wear.

    Mobile brings a constantly connected mindset, and it's driving changes in the way we—including our customers—consume content and interact with brands, from voice search to chatbots, and from digital assistants to the Internet of Things (IoT). 

    DNN Software has created an infographic that illustrates stats and figures related to this phenomenon. 

    For instance, the infographic shows that active users of virtual digital assistants are forecast to grow from 390 million (in 2015) to 1.8 billion by the end of 2021. Those digital assistants are driving an increase in voice searches. In 2016, Google announced that 20% of mobile queries are coming from voice searches, according to the infographic.

    To see more details about emerging technologies that are changing the way content reaches consumers, check out the infographic:


    Laura Forer is the manager of MarketingProfs: Made to Order, Original Content Services, which helps clients generate leads, drive site traffic, and build their brands through useful, well-designed content.

    LinkedIn: Laura Forer

  • 31 May 2017 9:20 AM | Kathy Doering (Administrator)

    By: Colin Shaw

    Founder & CEO | Customer Experience Thought Leader | Consultancy & Training | Keynote Speaker | C-Suite Network Member

    Economists hate surveys. They don’t trust them because people lie in surveys. Economists prefer to look at what people do instead, which, as we know, is what people are incented to do. Understanding these truths is critical to the work of an economist, and it is also vital to moving your Customer Experience to the next level.

    Let’s say you accept this premise, that people lie in surveys. However, you need to ask customers which product they value the most or how likely they are to do something in particular. How then should you get information about what people really want?

    According to Seth Stephens-Davidowitz, the answer lies in their online activity. Actions speak louder than words. It’s true in relationships, in customer interactions, and apparently in Google Searches.

     The Social-Desirability Bias

    Seth Stephens-Davidowitz is an economist, data scientist and an author. His book, Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are, explores how big data reveals the biases we have and how we think. After analyzing an assortment of data sources from online activity, his insight was that when we are alone with the internet browser, we express our real interests more than we do in a survey. It is true because of the social-desirability bias.

    The social-desirability bias describes how when we are taking a survey we answer the questions in a way we believe others will approve. Whether that means exaggerating the good, downplaying the bad or lying outright, we don’t want to share our undesirable behavior for fear of being judged, even on a survey. There is no incentive to be honest on the survey.

    However, those fears are assuaged when the search engine is open before us, full of answers. It won’t judge us for our queries or our interests. Moreover, lying to the search engine is pointless; we must tell the truth to get the information we need. So, the search engine incents us to tell it our secrets, which we do—and it records them all.

    The social desirability bias is the reason you can’t trust what your respondents say on a survey. In a recent Freakonomics podcast, Stephens-Davidowitz gave an excellent example of this concept. They asked people how they voted in the last election. Many who didn’t vote still responded with a candidate name because voting is socially desirable behavior. This behavior is why Stephens-Davidowitz says economists don’t like surveys. You can’t trust what people say; you have to look at what they do.

     Google Knows the Truth—And They Will Tell You, Too

    Google keeps track of what people search for and anyone can look at it. It’s called Google Trends. As they say on the Google Trends Page, “All our data is open source, so you can download and play with it for yourself.”

    Click here to see what today’s Google Trends are.

    As part of their advertising effort, you can also look at what the most common local and national searches are for specific subjects, including the number of searches for certain words or phrases.  Google provides it to help you build an online campaign using keywords. However, it is also insightful about what information people search for online.

    Click here to see how the general population is searching for your relevant keywords

    What people say they will do and what they do are often different things. It’s all part of how our irrationality influences our behavior. As in the survey example, it’s irrational to lie on a survey, especially when it’s anonymous or at least private. In many cases, the respondent will never interact with the researcher again, so any judgment will not have lasting repercussions. Stephens-Davidowitz explains that lying is a habit for people, so much so that “these behaviors carry over in surveys.”

     Embrace the Irrationality We All Share

    In my latest book, my co-author Professor Ryan Hamilton of Emory University and I share seven imperatives for moving your Customer Experience to the next level. The second imperative is to embrace the all-encompassing nature of customers’ irrationality. Part of this concept is the idea that people can always tell you why they did something; but they can’t always tell you why they really did something.

    I often refer to the story of how Disneyland guests told Disneyland researchers that they wanted the option of a salad at the park’s food venues. Disneyland added salads to the menu. However, when guests attend the park, they don’t order the salad; they order junk food. To me, this says that respondents don’t want the survey taker to know that they choose junk food over a healthier option. It is more socially acceptable to want a salad than junk, so that’s what respondents say.

    If you were to ask those same customers why they didn’t order a salad at Disneyland, they might say any number of reasons. Some of them might even be the truth. The majority, however, will probably be yet another version of the “truth.” Moreover, the person telling you their version of the truth might not even be aware of the lie they are telling. Irrationality is a cunning mistress.

    It seems like surveys are useless. But I wouldn’t go that far. Listening to what customers say is important. Surveys have their place and they do reveal a lot about customers, albeit mostly what customers want you to think of them. However, considering survey responses the whole truth, is a mistake.

    Instead, survey responses are only part of the truth. It is equally important to compare what customers say to what customers do. This insight will give you the opportunity to anticipate their needs and surprise and delight them in the Customer Experience.


  • 25 May 2017 9:58 AM | Kathy Doering (Administrator)

    Many of our social media feeds are dominated by beautiful, mouth-watering photos of food. These photos inspire some serious food envy but could they also educate and encourage healthier eating?

    That was the question explored by a team of researchers from the Department of Biomedical Informatics at Columbia University and the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). The results of the study were presented at the ACM SIGCHI Conference on Human Factors in Computing Systems in Denver, CO.

    "We know that humans learn by observing each other," said Marissa Burgermaster, a postdoctoral fellow at Columbia University and first author of the paper. "Our research explored whether we could leverage the popularity of online quizzes and food photos to develop new ways to teach people about nutrition."

    So, Burgermaster and co-author Lena Mamykina, Assistant Professor of Biomedical Informatics at Columbia University, teamed up with Krzysztof Gajos, Gordon McKay Professor of Computer Science at SEAS, to develop an online experiment to test how people learn about nutrition.

    Gajos is a pioneer in online experiments. In 2011, he co-founded LabInTheWild, an online platform for conducting behavioral research with unpaid, online volunteers.

    Behavioral studies performed in conventional labs are often constrained by time, size and a homogenous volunteer pool -- usually a group of undergrads from similar backgrounds. But online platforms like LabInTheWild can collect experimental data from a large group of volunteers from diverse backgrounds over extended periods of time.

    "Online experiments offer controlled but real-world environments to learn about the mechanisms of behavior," said Gajos. "These platforms offer a faster theory to experiment cycle and can provide more reliable data since the unpaid volunteers are motivated by curiosity or interest in social comparison."

    Using LabInTheWild, the researchers designed an experiment to test how people learn about nutrition in the context of a social, online quiz. The team was specifically interested in participants' knowledge of macronutrients, including carbohydrates, protein, fat and fiber.

    The experiment asked participants to compare photographs of meals -- such as split pea soup and black bean soup -- and identify which meal was higher in a specific macronutrient, such as carbohydrates.

    After each answer, participants received one of the following responses:

    No feedback at all

    The percentage of participants who chose each response

    The correctness of their answer without additional explanation from an expert

    The correctness of their answer with additional explanation from an expert

    The correctness of their answer with explanations written by fellow quiz-takers

    Later in the quiz, participants were asked to evaluate another pair of meals that included the same key ingredients to measure whether or not they learned anything from the feedback they received.

    About 2000 people participated in the experiment over a six-month period.

    The researchers found that, unsurprisingly, the participants who received additional information explaining the correctness of their answer did better on the quiz and learned more than participants who got no feedback or no explanations.

    However, the team also found that there was no significant difference between explanations generated by experts and explanations generated by peers.

    "These findings suggest that rather than relying on experts to teach nutrition literacy online, we can corral the wisdom of the crowd to help people make more informed decisions to improve their health," said Mamykina.

    "We're never going to be able to unleash an army of expert nutritionists to correct all the nutrition information on social media," said Burgermaster. "But we can tap into a larger, online social network. Our research shows that those social platforms could be used for learning and to nudge people towards healthy behaviors."

    "This research is another example of the transforming relationship between science and society," said Gajos. "By participating in an online experiment, participants gained knowledge that directly impacts their own life and decision making."



    Source:

    http://www.seas.harvard.edu/news/2017/05/learning-about-nutrition-from-food-porn-and-online-quizzes


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