Victor H. Gutierrez-Velez never expected his work to lead him to the topic of public health. His expertise lies in remote sensing science, analyzing data such as satellite images. “Every day, numerous satellite images are taken,” says Gutierrez-Velez. And the information drawn from these images has both academic and commercial applications.
For example, satellite images can help prescribe management, fertilization, irrigation, and other activities in precision agriculture, according to Gutierrez-Velez. They can help the insurance industry assess risks related to flooding or other natural disasters, or to verify crop insurance complains. Satellite imagery can allow energy companies to pinpoint the ideal location for solar panels. And this kind of data, it turns out, can even come in handy when it comes to fighting certain diseases.
To that end, partnering with colleagues with expertise in biology and public health, Gutierrez-Velez, assistant professor in the College of Liberal Arts, has recently been drawn to an unlikely research subject: mosquitoes. Specifically, the tiger mosquito (scientific name: aedes albopictus). What’s so interesting about this tiny, blood-sucking bug?
“It’s worrisome. They can spread the Zika virus and other dangerous diseases,” says Gutierrez-Velez.
In 2016 when the Zika pandemic caught his interest, mosquitoes dominated the headlines. Once thought to be limited to tropical and subtropical regions, the tiger mosquito had expanded its territory into most continents. Climate change plays a role, but these mosquitoes are also particularly aggressive. They’re among the 100 most invasive species in the world. In the 1980s, they were first spotted in the U.S. in Texas. Today, they reach as far north as Connecticut. Their presence in Pennsylvania remains an ongoing public health concern.
For his project, a recipient of the Office for the Vice President of Research‘s Targeted Grant Program and supported by the Data Science Institute housed at the Fox School, Gutierrez-Velez decided to look at multiple datasets, including climate data, information gathered from sampling for the presence of the tiger mosquito, land cover data, and census information. Gutierrez-Velez believes that with these and other datasets as inputs, machine learning and advanced algorithms can be used to predict the locations of tiger mosquito populations in advance of the season.
One of the most interesting possible findings of this research is that the tiger mosquito is less of a rural dweller than previously thought. “What we’re finding contradicts conventional wisdom about where these mosquitoes live. They are becoming domesticated animals. They prefer to be where lots of humans are living closely together—in cities. Because they love our blood,” says Gutierrez-Velez.
Scientific curiosity led Gutierrez-Velez to census data, which is not necessarily an obvious source of information to predict the presence or absence of a small flying bug. “If they feed on humans, human behavior should have something to do with it,” he says. And it does seem like including this data makes for a more accurate prediction about where the mosquitoes will go next.
Gutierrez-Velez’s ultimate goal for the project is to perfect a reliable working model that can be used to predict the upcoming mosquito season. Knowing that a particularly bad mosquito season is about to start will give officials the opportunity to plan in advance.
For example, the most affected areas can be targeted for treatment before the problem becomes unmanageable. Residents could be strongly cautioned in advance of the season to deal with housing-related conditions, such as places that collect standing water, which act as mosquito breeding areas. In the event that mosquitoes are spreading Zika or another virus, these protections could even save lives.
“There’s a lot we can do if we have a model that can say, ‘Hey, it’s going to be a bad year for mosquitoes, get ready,’” says Gutierrez-Velez.
This story was originally published in On the Verge, the Fox School’s flagship research magazine. For more stories, visit www.fox.temple.edu/ontheverge.
A personalized touch can make all the difference.
When you log into Amazon, Netflix, or Facebook, one of the first things you see are recommendations for products, shows, or friends you may know—all based on things you have already bought, watched, or liked.
Recommender systems have eliminated the time-consuming effort of understanding and anticipating what exactly users want, sometimes before they know they want it. Using vast collections of detailed data points, data scientists can create a trail of digital breadcrumbs, which follows Internet users as each sale, search, and interaction becomes part of an algorithm for new suggestions. These platforms can predict and encourage your next shopping sprees, binges, and bucket lists.
In the private sector, companies have long been using technology-enhanced learning in order to proactively suggest and anticipate their consumers’ choices. But how can these mechanisms be most effectively applied to academia?
“We have to learn something new every day,” says Dr. Konstantin Bauman, assistant professor in the Management Information Systems (MIS) Department at the Fox School of Business. “With the traditional path, we go to university, take some courses, meet with the instructor once a week, and go to lectures. But today, there are many different types of tools and materials available online that are able to educate large groups in a personalized and direct way.”
Educational companies like Coursera, Lynda.com and the Khan Academy already use recommender systems to suggest courses their users may like, based on their history. Bauman, however, wanted to know whether personalized e-learning can help students struggling to comprehend a particular subject.
“Education is one important application of recommender systems for society to target materials for specific learning paths,” says Bauman.
Bauman, along with co-author Alexander Tuzhilin of New York University’s Stern School of Business, examined the personalized e-learning systems approach with the help of a tuition-free online university. By using curricula from 42 classes and test results from 910 students over three semesters, the researchers created a system to pinpoint—and address—specific areas within a student’s comprehension that needed improvement.
The team reported their findings in the paper, “Recommending Remedial Learning Materials to Students by Filling Their Knowledge Gaps,” which was published in MIS Quarterly in 2018.
In this real-life experiment throughout the 2014-2015 academic year, Bauman’s team identified where students’ knowledge waned and provided materials to supplement these gaps. “Instead of revising the entire lesson, we provided catered lessons to fill those gaps,” says Bauman.
The students had diverse backgrounds, from both the United States and developing countries, and studied in a variety of programs, from business to computer science to art history. The researchers split the students into three groups: a control group that received no recommendations, a group that received non-personalized recommendations, and a group that received recommendations tailored to the individual student.
Bauman and his team created taxonomies that mapped all the topics covered within a specific course, built a library of remedial learning materials, and matched test questions with course topics. After analyzing test scores, the researchers identified the students’ weaknesses. The students in the non-personalized group received generic recommendations for the course, while students in the personalized group received remedial materials for specific topics that were identified via testing.
“First, we showed that most of the students who received our recommendations found them relevant and helpful,” says Bauman. Second, the “average” students, who received a test score between 70 and 90 in previously taken courses, were most affected by personalized recommendations. “These students improved their performance on the final exams significantly more, in comparison to their prior performance before they received personalized recommendations than the students from the control group.” For this subset of students, the personalized group received an average grade of 83.22 in their final exams, while the control group scored an average of 79.39.
The study received limited interactions with students who were classified as “falling behind” (those whose previous grade averages were below 70) as only six students who received personalized recommendations actually clicked on the materials. Similarly, students who were “excellent” (with average grades above 90) were less likely to need remedial lessons.
Bauman found that, by determining specific materials needed to supplement their understanding, students saved time and energy in preparation for their exams.
“Learning systems have the capability of picking up patterns and behaviors that can clearly predict necessary methods that are worthwhile and timely,” says Bauman. For students and professors, time that may be used to teach a specific lesson can be accomplished through recommender systems, saving more time for interactions that encourage new ideas and understandings.
One thing is for sure—when it’s time to come back for more, a new suggestion will be waiting.
This story was originally published in On the Verge, the Fox School’s flagship research magazine. For more stories, visit www.fox.temple.edu/ontheverge.
More than 15 million adults struggle with alcohol addiction. In fact, according to the CDC, one in ten deaths of working-age adults in America is linked to alcohol. That’s one reason data on alcohol use has been chosen by researchers for study from the enormous data set from the U.S. Department of Veterans Affairs’ ambitious Million Veteran Program (MVP). The VA intends as the project’s name states, to gather data on an astonishing one million service members.
Kuang-Yao Lee, assistant professor of statistical science at the Fox School, sees a world of potential new knowledge in this vast cache of data. This is particularly true of alcohol use because the data from the MVP is longitudinal, which means the same measurements are tracked over time. Alongside the support from the VA, Lee’s project received funding through from Office for the Vice President of Research at Temple University.
Volunteers in the MVP each submit blood samples as well as health surveys, amassing a dataset that comprises both genetic data and behavioral patterns. Beginning in 2016 when he was a researcher at Yale University, Lee and his colleagues have been using this information-rich resource to search for the specific combination of genes that correspond to alcohol and other substance use.
“Previous studies have suggested [these genes exist], but mostly were only limited to small scales or restricted conditions,” says Lee. “We want to use statistical models to find out if this is really a valid assumption. Our results so far suggest a very strong association.”
While ample electronic health records and genetic data have long been available to researchers, only recently has the efficient computing power become available to slice and dice the information into accurate, usable new insights and discoveries. More sophisticated algorithms combined with larger-than-ever computer storage capacity, as well as parallel computation techniques, allow today’s researchers to make meaning from a huge amount of complex data.
How huge? “Depending on the facility, the whole genome sequencing [for one person] can produce hundreds of millions of variants,” says Lee. Questionnaires allow researchers to gather large amounts of information about each subject every time they are administered. Multiply that by one million veterans. “We’re talking about not just billions, but millions of millions of points of data,” he says.
Data with this level of complexity can lead to findings that are more nuanced and reliable than in the past. Previously statistics sometimes led to oversimplified and other not-quite-right conclusions. We’ve all heard the old axiom, “There are three kinds of lies: lies, damned lies, and statistics.” But as so-called big data increases in scope and complexity and the tools used to analyze this data become more sophisticated, statistics are becoming more honest than ever before. From projects such as the Million Veteran Program and other similarly vast datasets, new genetic truths may ultimately emerge.
There are many possible real-world applications for this research. For one thing, determining which specific genes are linked with alcohol and other substance abuse could lead to new and better medicines and treatments for the very veterans who have volunteered their most sensitive personal information for this work. A dialed-in genetic profile that indicates a vulnerability for substance abuse could be used to screen kids and even adults while there is still time for effective early interventions that can keep them on a healthy path. Given the current public health crisis around opioids, alcohol, and other substance use, a breakthrough of this kind could have far-reaching benefits.
Lee says that the knowledge gleaned from the Million Veteran Program about substance abuse may lead to similar projects that could help solve other vexing behavioral, health, and genetic puzzles. He also notes that the innovative statistical models and tools he’s used in this research could be applied in myriad ways to other complex datasets.
For example, online shopping platforms can easily observe huge amounts of individual consumers and, at the same time, collect data across large numbers of variables. “One of the core problems in business analytics is to use statistical models to study the inter-dependency between observed variables, for example, the dependency between decision making and consumer behavior,” Lee says.
“There are a surprising number of similarities between genomics and online shopping.”
This story was originally published in On the Verge, the Fox School’s flagship research magazine. For more stories, visit www.fox.temple.edu/ontheverge.
NAO came to Temple University about three years ago, when Li Bai and Carole Tucker, researchers from the Colleges of Engineering and Public Health, and, joined Heidi Grunwald, director of Temple’s Institute for Survey Research, to study robotics and surveys.
The team wanted to explore a big idea: What if NAO, this cute, two-foot tall, human-ish robot, could be programmed to give health surveys to children on the autism spectrum? Could they create a system to collect patient-reported outcomes in this tough-to-survey population?
Potentially, this research could solve a number of difficult problems. Currently, its parents fill out surveys on behalf of their kids. Researchers would prefer patient-reported outcomes. It’s much more accurate than information filtered through a third party, such as a parent. “Kids with autism may be willing to say they are depressed, but not in front of their mother,” says Tucker. When a one-foot-tall robot with a cute robotic voice such as NAO is asking questions instead of a human clinician, researchers might get reliable patient-reported outcomes in a way they have not been able to in the past.
The team’s research would also include another stream of valuable information: para-data. The camera inside the robot would “watch” the subject as NAO asked the survey questions. Additionally, via the sensor the subject wears (a Microsoft wristband), researchers can monitor things like facial expression, heart rate, and body motion. This para-data is a rich vein of knowledge, particularly when combined with the survey questions, response time, and answers.
If the subject pauses an extra long time when a certain question is asked, the NAO can play a game (like rock, paper, scissors), take a break, or give a high five to reduce anxiety. This is one way that the robot uses para-data to adapt to a child’s answers. The para-data also helps the researchers better understand survey responses. “For example, we can tell if a particular question made a subject nervous and then down-weight the answer, or not count it,” explains Grunwald.
“The robot’s face is much less complex than a human face and human facial expressions,” says Tucker. That makes it much less overwhelming for a young person on the autism spectrum who may find it challenging to read people’s faces and maintain eye contact.
Their project received funding through the Office for the Vice President of Research’s Targeted Grant Program at Temple University, with matching funds from all three represented schools.
Planning the research has been an iterative process. On the computer science side, Bai and his engineering students have been exploring the feasibility of using NAO this way. Bai and the students have been answering questions like, “What features would be nice? How can we use sensors to pull in data and incorporate the Microsoft band?” They have iterated and refined the data architecture, a database where the data are meshed together so that the robot can read all of the survey response data–coupled with the para-data (sensor data).
Meanwhile, the questionnaire and para-data collection process have been tested on different groups, starting with older kids not on the autism spectrum. As trial subjects, children aged 10 and up can provide specific, meaningful feedback on interacting with NAO. More recently, a community event brought a group of children on the autism spectrum to campus, and the team had an opportunity to see NAO interact with the intended study subjects. Going forward, they have the kind of pilot data that can win the funding to drive this effort forward.
This work can benefit groups beyond younger people on the autism spectrum. Any time data reported directly from a patient may be skewed or inaccurate—such as dementia patients, for example—the survey methods used in this work could prove enormously helpful to clinicians.
“With future improvement in this type of research, I think we will see more robotic diagnostic platforms that will be developed. One of the functionalities will be surveys of the patients about their health conditions. It could be particularly important for people who can not find a good hospital in their neighborhood,” says Bai.
Practical applications of this work in the future aren’t limited to the realms of research and medicine by any means. “Artificial intelligence and robotics will be the next technology push to drive the economy of our country, There will be countless business world applications—such as personal robotic assistants (such as the iPhone’s Siri) or self-driving cars,” says Bai. He believes that the technology in this project will fuel innovations across all sectors of the economy in the years to come.
Massive amounts of data are created each day. With more than ever available at our fingertips, we need help to make sense of it all. The field of data science unites researchers across disciplines, who extract knowledge from unfathomable quantities of datasets. Whether helping business executives make data-driven decisions, advertisers target likely customers, or teachers identify knowledge gaps in students, the data scientists at the Fox School sort through the noise to discover groundbreaking insights. Learn how in the Fox School’s flagship research magazine On the Verge featuring Data Science.
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How a Robot Reaches Kids on the Autism Spectrum
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Etsy—the online treasure chest for all things handmade—cultivates a community for those who have a knack for crafts like candle-making, knitwear, jewelry, or pottery. With over 1.7 million active vendors and close to 28.6 million active consumers, Etsy has established a peer-to-peer business platform that eliminates the middleman of corporate production. Yet this marketplace is more than just an e-commerce site; it is a community of like-minded individuals who appreciate handicrafts.
Within the site, buyers and sellers interact through a variety of IT-enabled features, like following and messaging shops, reviewing and favoriting products, and curating lists of products. Yet as sellers socialize by favoriting and promoting others’ products, are they redirecting potential customers away from their shops?
Professor Sunil Wattal and doctoral student Ermira Zifla of the Management Information Systems Department at the Fox School of Business investigate how social mingling affects e-commerce marketplaces in their paper, “Understanding IT-enabled Social Features in Online Peer-to-Peer Business for Cultural Goods.”
“What really fascinated us about this platform is that you have this community aspect, but you are also introducing this e-commerce agenda,” says Wattal.
“We thought that sellers may have mixed incentives to participate in the online community,” adds Zifla. “On the one hand, participating by following others and posting in forums may increase the visibility of sellers and subsequently increase their sales. On the other hand,” she continues, “following other sellers and sharing their products could negatively impact sales by diverting traffic away from their own page.”
While online communities have often been the subject of research, this is one of the first studies to link social indicators with economic performance. Using a dataset of nearly 2,000 sellers on Etsy, Wattal and Zifla examined their interactions in the online community and found how socializing with others can inherently affect a shop’s sales.
The researchers identified two categories of social e-features that promote new products and validate users:
1. Community participation features—such as following other sellers and joining teams—which facilitates socializing with other members, and
2. Content curation features—such as curating favorite lists, sharing products, and favoriting shops—which serve as tools for validation and tastemaking.
“When you are following other people on Etsy, those people are listed on your page as a form of validation, for what you like to buy as a consumer or what you can provide as a producer,” said Wattal.
The researchers hypothesized that community participation and content curation would increase a seller’s online status by increasing their number of followers, but would decrease a seller’s sales by diverting attention away from their own products.
Using a web crawler to collect public information, the pair obtained a dataset of 1,728 unique glass sculpture sellers—a randomly chosen subcategory of marketplace shops on Etsy—to compile a year’s worth of data, including sellers’ followers, lists, favorited products, and sales.
Analyzing the data proved the researchers’ hypotheses correct: a 10 percent increase in community participation, like following other sellers, and content curation, like favoriting products, resulted in a 3.89 percent decrease in sales. Yet this reduction was outweighed by the effects of cultivating a stronger social following. In other words, the same activities that led to a direct decrease in sales helped sellers attract more followers, and were associated with an indirect increase in sales by 4.64 percent—an overall net gain.
“IT-enabled features have benefits that supersede the negative,” says Wattal, “since exposure is what can ultimately lead you to be on an influential list or you can simply commercialize yourself to the point of high-status.”
Trends can come and go as quickly as a trendsetting blogger changes her mind. Yet in the realm of vintage trinkets and artisanal finds, relationships stay relevant.
This story was originally published in On the Verge, the Fox School’s flagship research magazine. For more, visit www.fox.temple.edu/ontheverge.
Data-driven decision-making is now the norm in many workplaces. Executives collect and analyze information to inform hiring practices, promotions, and insurance premiums. However, Leora Eisenstadt, assistant professor of Legal Studies at the Fox School, warns that the kinds of data that employers can track should be safeguarded by law, to both protect employees’ privacy and limit employers’ liability.
For many, work and personal time have begun to blur together as smartphones and emails have invaded the home. As this line erodes between the home and office, employees are often left unaware that their employers can glean so much information from their personal lives. “Most of us have left enormous data trails,” says Eisenstadt, “that employers are now beginning to access in order to create the most efficient workplaces possible.”
With social media, FitBits, and online healthcare platforms, Eisenstadt says, employers are gathering data from more than just workplace activities. Healthcare service platforms, for example, can tell by looking at internet searches, prescriptions changes, or specialist appointments that employees are planning to start a family or have major surgery.
The platforms indicate that only top-level numbers are shared with employers, not individual names of employees. However, she argues, “that knowledge could lead to companies making decisions about promotions, hiring, and terminations based on this information.” Narrowing down gender and age, for example, could give employers enough clues to know which of their employees were likely to be trying to have a baby soon.
In her paper, “Data Analytics and the Erosion of the Work/Non-Work Divide,” which was accepted for publication by the American Business Law Journal, Eisenstadt asserts that the current legal statutes do not provide enough protection to both employers and employees. “Laws like HIPAA and the Pregnancy Discrimination Act likely do not apply to data gleaned from search queries,” she says. And there are virtually no laws or regulations prohibiting employers from collecting and relying on data gleaned from employees’ social media profiles, from facial recognition software, or from Fitbits.
So why should employers care about overreaches into employee privacy?
“The erosion of the work/non-work divide will impact the concept of a ‘scope of employment’ and employer attempts to avoid liability for their workers’ actions,” says Eisenstadt. Over the years, courts have seen the line blur between personal and work-related activities—like a case in 1928 in which an auto sales manager crashed a car, killing an employee on the way home from a staff appreciation dinner. The courts found the company liable for the death, and considered the events to be “within the scope of employment.” This move toward an expanding “scope of employment” has only grown with the advent of laptop computers, smartphones, and the myriad other devices and technologies that make it easier and sometimes even essential to bring work outside of the traditional physical boundaries of the workplace.
By gathering data from nonwork activities, Eisenstadt cautions that employers may be pushing this trend to new, more troubling places. By eroding the work/non-work divide so dramatically, companies may be opening themselves up to new liabilities for employee health issues, violent outbursts, or other employee behavior that would previously have been considered to be outside the “scope of employment.”
Data analytics can be an extremely powerful tool. “It allows humans to capture, analyze, and use massive quantities of data,” says Eisenstadt, “that the human brain can not make sense of on its own.” Yet, in today’s environment of data concerns and privacy breaches, Eisenstadt warns, companies should be cautious of data mining that goes too far.
It’s the moment every woman dreads: A routine breast self-examination during an otherwise relaxing shower ends in the panic-inducing discovery of a lump.
Often, what happens next is a long, harrowing journey through a combination of biopsies, surgery, chemotherapy, and radiation. While it’s true that, thanks to advancements in screening and treatment, more and more women survive breast cancer, it’s also true that 80 percent of breast cancer cases have already advanced to an invasive stage at the time of diagnosis.
Today, just 20 percent of breast cancers are identified at the earliest stage, when treatment is most effective and the five-year survivorship rate hovers near 100 percent.
Carlos Barrero, MD, and Oscar Perez-Leal, MD, assistant professors in the Pharmaceutical Sciences Department at Temple University’s School of Pharmacy, wants to change all that. “I believe we can invert those numbers so we’re discovering 80 percent of breast cancers at the very earliest stage,” he says.
The research Barrero and Perez-Leal are conducting may represent a major breakthrough in breast cancer screening. Their work could lead to a simple routine blood test that detects breast cancer sooner than ever before for more women. To do this, Barrero and Perez-Leal are working on identifying a set of biomarkers for breast cancer, a specific signature of early-stage breast cancer detectable in a blood sample.
Their work on this project received funding through the Office for the Vice President of Research’s Targeted Grant Program, and the team is currently in the process of securing additional funding from the National Institutes for Health, and the National Cancer Institute. Perez-Leal is also using the knowledge gleaned from his master’s degree from the Fox School’s Innovation Management & Entrepreneurship program to turn the idea into a feasible product.
Though mammograms are a recommended cancer screening for women age 40 and older, only 65 percent of women over 40 have had one in the past two years, according to the Centers for Disease Control.
“Many women avoid mammograms because they can be uncomfortable, and because of the hassle of needing to make a separate appointment. If screening for early-stage breast cancer became a part of routine blood work, more women would be screened regularly,” says Barrero. That would likely result in more early diagnoses, more effective treatment, and ultimately more long-term cancer survivors.
Through systems biology, advances in mass spectrometry technology that allow the detection of very low concentration of proteins and metabolites, and the availability of large public datasets from thousands of breast cancer tumors, Barrero and Perez-Leal can move this cutting-edge work forward. “Most research of this kind starts with analyzing the blood sample. We start by analyzing the data,” says Perez-Leal. It’s a fresh approach to a longstanding problem.
The researchers start by looking for specific proteins secreted by breast cancer tumor cells across many thousands of samples drawn from breast cancer tumors. The team is searching for a signature set of proteins that can be detected in very low amounts. A vast data set and formidable computing power are essential for finding the precise biomarkers that could, in five to 10 years, lead to the blood test. Recently upgraded mass spectrometry equipment at Temple’s School of Pharmacy allows him to carry out this innovative research.
The promise of this research extends even beyond the hopes of early detection into the possibility of new, more effective medicines to battle breast cancer. Going forward, biomarkers are likely to be an increasingly hot topic for those in the pharmaceutical industry, which represents a significant part of the U.S. economy. Biomarkers such as these are often used as a reference point in drug development; when the biomarkers diminish or disappear in blood tests, it’s evidence that the new drug is working.
Current treatments for breast cancer are effective, but they come with their own health risks and side effects, some of which lead to different health challenges years after patients have recovered from cancer. The identification of these biomarkers would also mean that, in addition to early intervention, a breast cancer patient could get a form of personalized medicine, which is another area of potential business growth for the pharmaceutical industry. For patients, that might mean fewer side effects and complications down the line.
“It’s rare to find a scientist with a business background,” says Perez-Leal. He praises the Innovation Management and Entrepreneurship program with helping him take an idea, establish a business plan, and pitch to investors. “The research community should continue to focus on finding solutions and products to real problems.”
Clearly, breast cancer is a real problem, as the most common cancer among women: one in eight will face a diagnosis in her lifetime. But if Barrero and Perez-Leal succeed, it will be a game-changing advance. Many more women will be diagnosed in cancer’s earliest stages, receive more personalized treatment, overcome the disease, and lead long and healthy lives.
How much is a hashtag worth to you?
This simple symbol has become ubiquitous across many social media platforms. Started in August 2007, the hashtag, also known as the pound (#) sign, was officially adopted by Twitter in 2009 as a way to group conversations and aggregate similar themes. Now, having spread to sites like Instagram, Facebook, LinkedIn, and Pinterest, the hashtag has become a key element of many companies’ social media strategies. With that pervasiveness comes power—and pitfalls.
“Creating an original hashtag gives a firm control over a specific social media space,” says Subodha Kumar, professor of Marketing and Supply Chain Management at the Fox School. Businesses can use this tool to increase recognition of their brand, generate buzz, and expand their audiences.
Yet creating a hashtag does not automatically mean the company owns it, says Kumar. Hashtags are susceptible to hijacking, in which competitors or consumers use the hashtag for unofficial messaging—like when McDonald’s attempted to generate positive publicity with #McDStories but instead received thousands of complaints about the fast food chain.
So, how can a company protect its social media reputation? For some, the answer lies in trademarking.
“The trademark protection of hashtags can increase consumer confidence,” says Kumar. Since the U.S. Patent and Trademark Office began allowing hashtags to be registered trademarks in 2013, more and more companies are protecting their intellectual social media property. In 2015, nearly 1,400 hashtags were submitted in trademark applications. “It prevents other competitors from using similar hashtags to mislead consumers.”
However, trademarks may come with a price. “Trademarking a hashtag may prevent or restrict its use,” Kumar says. The successful spread of a hashtag lies in its ability to be used by anyone, connecting millions of Twitter threads and Instagram photos into one conversation. By trademarking, companies could be stifling this kind of organic engagement.
Little research has been done to understand whether a trademarked hashtag makes a firm’s social media audience more or less engaging. Kumar, along with Naveen Kumar of the University of Memphis and Liangfei Qiu of the University of Florida, wanted to know: does trademarking a hashtag defeat its original purpose?
Kumar and his co-authors investigated the tension in these two opposing sides—the organic nature of a hashtag and the restrictive nature of a trademark—in their paper, “A Hashtag is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags.”
The researchers compared firm-level tweet data from 102 companies, split between a “treated” group of companies who had trademarked a hashtag between 2014 and 2017 and a “control” group of similar firms. The study compared tweets from before and after the hashtag’s trademark approval, analyzing the level of engagement through likes, comments, and tweets, as well as the linguistic content of the tweet, including its emotions, tone, and style.
Based on this study, Kumar and his colleagues discovered some key factors of making a trademarked hashtag work for a company:
1. Companies that trademark hashtags have higher social media engagement.
This study is the first to identify that trademarking hashtags can improve firms’ engagement with its audiences on social media—though the effects have varying levels of intensity for different types of firms and social strategies. “Trademarking a hashtag can increase the number of retweets by 27 percent,” says Kumar, “which is a considerable amount.”
Yet firms can not trademark hashtags arbitrarily. The U.S. Patent and Trademark Office treats hashtags like any other trademark: in order to be approved, the company needs to prove that the hashtag is a key part of the firm’s identity and that trademarking works in the consumers’ favor by preventing or reducing confusion.
2. Trademarking hashtags works better for smaller, less popular companies with fewer Twitter followers.
While the study demonstrates that trademarking increases social media engagement, Kumar and his colleagues investigated how this effect varies among different types of firms. After comparing the companies in the top and bottom percentiles in terms of Twitter followers, the researchers found that firms with fewer Twitter followers had more significant increases in their engagement after trademarking hashtags than companies with larger followings.
Kumar hypothesizes that small companies see larger positive effects because fewer consumers are aware of their brands and products. “Without trademark protection, other competitors can easily use similar hashtags to mislead consumers,” he says. “In contrast, for popular firms with more Twitter followers, it is more difficult to mislead consumers, even in the absence of trademark protections.”
3. Writing styles are more important to firms that use trademarked hashtags.
The researchers also studied how companies used language in their social media strategies to understand the key drivers that cause trademarking hashtags to increase engagement. “This is based on the assumption that the way that people use words reflect how they think,” says Kumar. For example, using pronouns can reflect a self-centered focus, or using prepositions and conjunctions can indicate more nuanced thinking.
The study found that when hashtags are trademarked, a firm’s writing style becomes more important to its social media engagement. “People tend to like a more narrative and informal writing style in tweets,” Kumar says. The researchers saw that more positive, colloquial, and confident writing increase retweeting by up to 10 percent.
4. Effects of increased social media engagement last longer when hashtags are trademarked.
Recognizing that trademarking is a lengthy and expensive process, the researchers sought to discover whether the increased engagement lasted in the long term.
“Before trademarking hashtags, writing more tweets with desirable linguistic styles has only a contemporaneous effect,” says Kumar, meaning that the tweets’ increase in engagement was immediate, but dropped off quickly. After one month, it was no longer significant. “Trademarking hashtags makes things different,” Kumar says. After trademarking, the researchers found that the effects of increased engagement were still happening a month later.
Based on their research, Kumar and his colleagues believe that, especially for smaller companies with fewer followers, trademarking their intellectual social property, like hashtags, is a worthwhile investment. However, to get the maximum bang for your buck, Kumar suggests that companies consider the longevity of their chosen hashtag.
Social media can be fleeting, so invest wisely.
To swipe or not to swipe?
Online dating has come a long way since the days of OKCupid in the early aughts. Today, phrases like “Tinder date” have become part of society’s lexicon, and we have stopped buying a stranger a drink in a bar and started double tapping an Instagram photo from home.
What is different today? Instead of logging into a dating site on a computer, romance seekers now have mobile apps at their fingertips.
JaeHwuen Jung, assistant professor of Management Information Systems (MIS) at the Fox School of Business, investigated the changing business behind online dating to learn why companies are spending more money on developing mobile applications instead of web platforms.
With apps like Tinder and Bumble, data scientists have a trove of unbiased data from which they can extract insights. “We are able to trace the actions of both parties,” says Jung. “We are able to see who is meeting who, what type of profiles they have, and [what] sort of messages they are exchanging.” This provides a unique opportunity for researchers to analyze data untainted from other collection processes, like simulated experiments.
Jung says that dating is only one of many examples of how our phones have completely transformed the way in which we behave—and companies have caught on.
In his paper, “Love Unshackled: Identifying the Effect of Mobile App Adoption in Online Dating,” which has been recently accepted for publication at MIS Quarterly, Jung used the online dating world to identify three drivers of why users, and subsequently companies, are moving from web to mobile: ubiquity, impulsiveness, and disinhibition.
- Ubiquity: the capacity of being everywhere, especially at the same time
- Impulsiveness: having the power to be swayed by emotional or involuntary impulses
- Disinhibition: a lack of restraint and disregard to social norms
With the ubiquity of smartphones, users are able to access mobile apps at any given time and location. Features like instant notifications, location sharing, and urgency factors, like Tinder’s daily allowance of five ‘Super Likes,’ have allowed users to stay constantly connected.
“We use our mobiles in the most personal locations, like our beds and bathrooms,” says Jung. For some, their phones may seem surgically attached to their hands.
With phones constantly by their sides, people more readily give in to their impulses, reacting to their moods or thoughts instinctively. Users can respond to such feelings—such as responding to a flirtatious message or liking a post—without a second thought.
“We found that [mobile platforms] change users’ daily lifestyle patterns,” says Jung. “Compared to those who use web platforms, mobile users have the luxury to log on earlier, later, and more frequently.”
When a sense of privacy is assumed, users feel more anonymous on mobile—and are thus less likely to follow social norms. This disinhibition creates higher levels of engagement on mobile devices, Jung found, as users were more likely to engage in actions that they were less likely to do outside of the app.
“We saw that replies and views of [profiles of people with] different races, education levels, and even height, became more apparent through mobile apps,” says Jung. “This has us questioning, can this [disinhibition] change viewpoints in real life?”
Like any business plan, owners try to keep customers coming back for more. These three key features—ubiquity, impulsiveness, and disinhibition—help companies keep users online every time they unlock their phones. With the convenience provided by apps, dating has become more successful for users and has benefited companies as well.
“If people leave happy,” Jung says, “they will bring more new customers [to the app.]”
With the surge of app monetization, developers are able to make 55% of their mobile revenue through video ads, display ads, and native ads, according to Business Insider. Mobile apps have become a win-win situation as more people choose to scroll on the go.
Jung’s paper is the first of its kind to examine the causal impact of companies’ mobile channels in addition to their web presence. What can we say? All’s fair in love, war, and big data.
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