Machine learning tools analyzed tweets about COVID-19 and showed that social media can significantly influence people’s health beliefs.

 Using machine learning, researchers found that people’s biases about COVID-19 and its treatments are exacerbated when they read tweets from other users, a study published in JMIR showed.

The analysis also revealed that scientific events, like scientific publications, and non-scientific events, like speeches from politicians, equally influence health belief trends on social media.

The rapid spread of COVID-19 has resulted in an explosion of accurate and inaccurate information related to the pandemic – mainly across social media platforms, researchers noted.

“In the pandemic, social media has contributed to much of the information and misinformation and bias of the public’s attitude toward the disease, treatment and policy,” said corresponding study author Yuan Luo, chief Artificial Intelligence officer at the Institute for Augmented Intelligence in Medicine at Northwestern University Feinberg School of Medicine.

“Our study helps people to realize and re-think the personal decisions that they make when facing the pandemic. The study sends an ‘alert’ to the audience that the information they encounter daily might be right or wrong, and guide them to pick the information endorsed by solid scientific evidence. We also wanted to provide useful insight for scientists or healthcare providers, so that they can more effectively broadcast their voice to targeted audiences.”

Researchers set out to evaluate individuals’ COVID-19-related health beliefs on Twitter. The team retrospectively collected COVID-19-related tweets using the Twitter API. In total, they gathered 92,687,660 tweets corresponding to 8,967,986 users from January 6 to June 21, 2020. To train the machine learning model, the team randomly selected 5,000 of the tweets for annotation.

Researchers used machine learning to review each tweet doubly to determine if they met any of the four core constructs of the health belief model (HBM), a framework developed to investigate people’s beliefs about health problems. The HBM’s four core constructs include perceived susceptibility, perceived severity, perceived benefits, and perceived barriers.

The results showed that the machine learning tools achieved areas under the receiver operating characteristic curve of 0.86 for the classification of all four HBM constructs. The team pointed out that fluctuations in the number of health belief-related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events.

Specifically, researchers found that scientific events and non-scientific events were comparable in their ability to influence health belief trends on social media.

“Our findings demonstrated that trends in health beliefs were correlated with dynamics in positive case and mortality rates. Additionally, we observed a decline in perceived disease susceptibility during government-issued lockdowns, while perceived severity appeared unaltered. Lastly, our study identified top news events, scientific and nonscientific, that may play a role in altering health beliefs,” researchers said.

“These findings lay the groundwork to better understand how the general public’s COVID-19-related health beliefs are influenced by case and mortality rates, government policies, current news, and significant events.”

The group noted that this study is unique in that integrates machine learning algorithms with classic epidemiology models to retrospectively investigate the contents on social media and its effects. Researchers also worked to improve the machine learning model’s interpretability, allowing other investigators to understand how this algorithm works.

“We identified the fluctuating trends of public attitudes from the tweets, then aligned the important scientific and non-scientific events that are associated with these trends,” Luo said. “As a result, we are offering insights people can take action on.”

The research team is currently integrating machine learning and deep learning to understand how social media can impact the general public’s attitude toward COVID-19 vaccines. The overall aim of this effort is to identify specific public concerns and inform targeted vaccination campaigns to maximize inoculation impact. Additionally, the group is exploring the use of social media to detect gender and racial disparities during and beyond the pandemic.

The team expects that their study can help inform public health strategies for reducing the spread of COVID-19 misinformation.

“The excessive information disseminated on social media platforms and other sources is closely related to the dynamics of the general public’s health beliefs. The dynamics of the pandemic, news, scientific and nonscientific events, and even the related tweets already published on social media platforms may influence the health beliefs of the general public on social media to some extent,” researchers concluded.

“Our findings provide clues and evidence for more effective management of the infodemic associated with the COVID-19 pandemic.”

Data analytics tools and social media platforms have played a significant role in tracking the attitude of the public throughout the pandemic. In May 2020, a team from Penn Medicine showed that public health officials can use natural language processing techniques to track surges in interest in COVID-19 topics on online forums like Reddit.

“Public health priorities do not always align with community priorities, and the success of public health efforts often depends on having a plan to address community concerns,” said Daniel Stokes, a research fellow with the Center for Emergency Care Policy and the Center for Digital Health at Penn Medicine.

“Having a source like Reddit that is directly tied to people’s thoughts could prove invaluable in crafting plans that meet people where they are.”

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Big data in GIS has critical ramifications for how we procure and leverage spatial data

In the midst of the surge of data we gather and fight with consistently, geospatial information possesses an interesting spot. Because of the networks of GPS satellites and cell towers and the rising Internet of Things, we’re able to track and correlate the location of people and items in exact manners that were impractical up to this point. Yet, putting this geospatial information to use is more difficult than one might expect.

It is frequently said that 80% of data has a spatial part. Once in a while it is a coordinate gathered from a GPS application, or essentially an address that gets geocoded to a location along a street centerline. Regardless, it is surprisingly simple to get the location of an item. With moving items, location and time are imperative to follow the article alongside some other applicable attributes (temperature, point, size, shading, and so forth). As sensors and devices become increasingly connected, data is being gathered at an uncommon rate.

The Big data pattern has drastically affected each industry, so it is little amazement that big data in GIS has critical ramifications for how we procure and leverage spatial data. Big data is definitely not a new pattern. Notwithstanding, it is turning into a bigger part of geographic data science.

Maybe perhaps the greatest change in the discussion around big data has been in the relationship between software, hardware, and expertise. One of the foremost utilizations of geospatial big data analytics has been in the humanitarian area. GIS IoT gadgets are currently being utilized across the world to gather information in conditions which were previously hard for aid workers to access and thus hard to work in.

For an illustration of the manner by which geospatial big data analytics can function admirably in this area, consider by DigitalGlobe, a non-profit organization that sources satellite information and coordinates it with different sources like social  media notion and aerial imagery, use a GIS machine learning algorithm to follow activity in explicit areas and identify anomalies.

Geospatial information is not simply an area, nonetheless. Geospatial information likewise tracks how things are connected and where they are in relation to other objects. Realizing how an object changes over the long run corresponding to different items can give critical insights. For instance, how truck maintenance recommendations change depending on where a truck is found and how it is driven in the field? Utilizing all of your data to drive more intelligent maintenance plans sets aside cash, time and assets.

Robots, or unmanned aerial vehicles (UAVs) as the business calls them, have been everywhere on the news of late. What’s more, as you may expect, there’s a big data angle to them, particularly with regards to location intelligence and geographic information systems (GIS) products.

UAVs are emerging as an astounding method to accumulate data from the air. As per the Flightline Geographics auxiliary of ESRI partner Waypoint Mapping, UAVs can capture pictures with goals down to one inch, and convey that data in no time, compared to the days regularly needed by manned aircraft.

A couple of years ago, it was hard to envision how the financial sector and geospatial information would cooperate – there seemed, by all accounts, to be little value to a bank or other financial services company in knowing where their customers traveled and when.

Incidentally, this data is just as valuable to the financial sector as it is in other sectors. Truth be told, geospatial big data in the financial area presently plays a role in the progressing startup boom that plans to bring geospatial analysis procedures to the core of business decisions.

The applications are as yet being explored, however, as of now appear to be encouraging. Geospatial information has already been valuable, for example, in figuring out which branches to merge, as well as how satellite imagery over time can all the more likely foresee a property’s risk of flooding when it comes time to decide insurance rates.

Financial services firms are driving with regards to utilizing GIS and business intelligence tools together. For the financial industry, geospatial big data is playing a part in making a blast of a boom of startup companies. So many financial startups here have been advertising themselves for their capacity to use non-traditional data sources, for example, satellite imagery, for deciding the possible danger of offering insurance or a loan. For instance, satellite imagery throughout a range of time could more readily anticipate a property’s risk of flooding for determining insurance rates.

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Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).

The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

The research will be presented at next month’s Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han’s lab in MIT’s Department of Electrical Engineering and Computer Science. Co-authors include Han and Yujun Lin of MIT, Wei-Ming Chen of MIT and National University Taiwan, and John Cohn and Chuang Gan of the MIT-IBM Watson AI Lab.

The Internet of Things

The IoT was born in the early 1980s. Grad students at Carnegie Mellon University, including Mike Kazar ’78, connected a Cola-Cola machine to the internet. The group’s motivation was simple: laziness. They wanted to use their computers to confirm the machine was stocked before trekking from their office to make a purchase. It was the world’s first internet-connected appliance. “This was pretty much treated as the punchline of a joke,” says Kazar, now a Microsoft engineer. “No one expected billions of devices on the internet.”

Since that Coke machine, everyday objects have become increasingly networked into the growing IoT. That includes everything from wearable heart monitors to smart fridges that tell you when you’re low on milk. IoT devices often run on microcontrollers — simple computer chips with no operating system, minimal processing power, and less than one thousandth of the memory of a typical smartphone. So pattern-recognition tasks like deep learning are difficult to run locally on IoT devices. For complex analysis, IoT-collected data is often sent to the cloud, making it vulnerable to hacking.

“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” says Han. “Companies like Google and ARM are all working in this direction.” Han is too.

With MCUNet, Han’s group codesigned two components needed for “tiny deep learning” — the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system. TinyEngine is optimized to run a particular neural network structure, which is selected by MCUNet’s other component: TinyNAS, a neural architecture search algorithm.

System-algorithm codesign

Designing a deep network for microcontrollers isn’t easy. Existing neural architecture search techniques start with a big pool of possible network structures based on a predefined template, then they gradually find the one with high accuracy and low cost. While the method works, it’s not the most efficient. “It can work pretty well for GPUs or smartphones,” says Lin. “But it’s been difficult to directly apply these techniques to tiny microcontrollers, because they are too small.”

So Lin developed TinyNAS, a neural architecture search method that creates custom-sized networks. “We have a lot of microcontrollers that come with different power capacities and different memory sizes,” says Lin. “So we developed the algorithm [TinyNAS] to optimize the search space for different microcontrollers.” The customized nature of TinyNAS means it can generate compact neural networks with the best possible performance for a given microcontroller — with no unnecessary parameters. “Then we deliver the final, efficient model to the microcontroller,” say Lin.

To run that tiny neural network, a microcontroller also needs a lean inference engine. A typical inference engine carries some dead weight — instructions for tasks it may rarely run. The extra code poses no problem for a laptop or smartphone, but it could easily overwhelm a microcontroller. “It doesn’t have off-chip memory, and it doesn’t have a disk,” says Han. “Everything put together is just one megabyte of flash, so we have to really carefully manage such a small resource.” Cue TinyEngine.

The researchers developed their inference engine in conjunction with TinyNAS. TinyEngine generates the essential code necessary to run TinyNAS’ customized neural network. Any deadweight code is discarded, which cuts down on compile-time. “We keep only what we need,” says Han. “And since we designed the neural network, we know exactly what we need. That’s the advantage of system-algorithm codesign.” In the group’s tests of TinyEngine, the size of the compiled binary code was between 1.9 and five times smaller than comparable microcontroller inference engines from Google and ARM. TinyEngine also contains innovations that reduce runtime, including in-place depth-wise convolution, which cuts peak memory usage nearly in half. After codesigning TinyNAS and TinyEngine, Han’s team put MCUNet to the test.

MCUNet’s first challenge was image classification. The researchers used the ImageNet database to train the system with labeled images, then to test its ability to classify novel ones. On a commercial microcontroller they tested, MCUNet successfully classified 70.7 percent of the novel images — the previous state-of-the-art neural network and inference engine combo was just 54 percent accurate. “Even a 1 percent improvement is considered significant,” says Lin. “So this is a giant leap for microcontroller settings.”

The team found similar results in ImageNet tests of three other microcontrollers. And on both speed and accuracy, MCUNet beat the competition for audio and visual “wake-word” tasks, where a user initiates an interaction with a computer using vocal cues (think: “Hey, Siri”) or simply by entering a room. The experiments highlight MCUNet’s adaptability to numerous applications.

MCUNet: Visual Wake Word Demo on a $10 Microcontroller

“Huge potential”

The promising test results give Han hope that it will become the new industry standard for microcontrollers. “It has huge potential,” he says.

The advance “extends the frontier of deep neural network design even farther into the computational domain of small energy-efficient microcontrollers,” says Kurt Keutzer, a computer scientist at the University of California at Berkeley, who was not involved in the work. He adds that MCUNet could “bring intelligent computer-vision capabilities to even the simplest kitchen appliances, or enable more intelligent motion sensors.”

MCUNet could also make IoT devices more secure. “A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”

Analyzing data locally reduces the risk of personal information being stolen — including personal health data. Han envisions smart watches with MCUNet that don’t just sense users’ heartbeat, blood pressure, and oxygen levels, but also analyze and help them understand that information. MCUNet could also bring deep learning to IoT devices in vehicles and rural areas with limited internet access.

Plus, MCUNet’s slim computing footprint translates into a slim carbon footprint. “Our big dream is for green AI,” says Han, adding that training a large neural network can burn carbon equivalent to the lifetime emissions of five cars. MCUNet on a microcontroller would require a small fraction of that energy. “Our end goal is to enable efficient, tiny AI with less computational resources, less human resources, and less data,” says Han.

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Amazon Web Services partnered with social media platform developer RallyPoint, the Department of Veterans Affairs and Harvard University to develop a machine learning model that can detect mental health issues among veterans, Nextgov reported Friday.

Under an agreement with VA, the Amazon Machine Learning Solutions Lab worked with RallyPoint and Harvard’s Nock Lab mental health professionals to utilize data science for identifying “high-value use cases” among users of the military-focused social network.

AWS provided data-labeling services as well as its SageMaker managed-service platform to train a machine-learning model to detect signs of risk in anonymous public posts on RallyPoint.

The team is accepting feedback on the effort and plans to further develop the model in the coming months. They also seek to provide RallyPoint users with access to mental health programs, support groups and hotlines, according to the report.

Dave Gowel, CEO of RallyPoint, said the effort is aimed at supporting government efforts to address veterans’ mental health issues through programs such as VA’s Suicide Prevention Program and the President’s Roadmap to End a National Tragedy of Suicide.

The team effort builds on RallyPoint’s memorandum of understanding with VA to improve veteran interactions with communities signed in 2018.

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The concept of Big Data has been around for some time now, as most of the business organizations have started to understand the benefits it brings to the table. Data Analytics is becoming more of a higher priority for businesses, as more data is generated every day over the internet. On the Social media front alone, Facebook generates 4 million Gigabytes of data every day, including 4 million likes every minute. On the other hand, Instagram generates 95 million posts daily, which includes 2 million posts from advertisers.

With such a high magnitude of data generated on a daily basis, it becomes impossible to keep up without some sort of data analytics. Many businesses have understood the strategic importance of Data Analytics and are heavily investing in it. More than 75% of the companies are utilizing data analytics to their advantage at present. Tech giants such as Google and Tesla have broken into new markets, leveraging data analytics.

Advantages Big Data Analytics Offer

Most organizations today have Big Data at their disposal, and understand the need to harness it. Big Data Analytics helps organizations harness the data and use it to identify new opportunities in the market. It leads to efficient operations, smarter business moves, higher profits, and happier customers. Big Data Analytics adds value to businesses in the following ways:

Better decision making

Thanks to Data Analytics, Organizations can better understand their audience and recognize what strategies would be successful in marketing their brand. This is where engagement metrics come into play. Organizations are now capable of knowing their consumers’ opinions, especially on the areas concerning likes and dislikes. Marketing content and campaigns are then tailored to perfection to the target audience.

New products and services

The ability to gauge customer needs and satisfaction through analytics gives businesses the power to offer customers what they want. According to Davenport, companies are creating new products to meet customer needs using Big Data Analysis at a scale never seen before.

Cost reduction

The advent of cloud-based analytics tools has brought significant cost advantages when it comes to storing large amounts of data. Amazon, Microsoft, and Google with their cloud storage services are at the forefront when it comes to reducing costs through judicious use of Data Analytics.

Enhanced customer experience

The fundamentals of business have not changed – meeting the customers’ needs is still the ultimate objective of any organization. Today, there is more information available than ever before. Data mined from websites and social media can be used to form a complete view of customer behavior and patterns. If used correctly, Data Analytics enables businesses to know more about consumers than ever thought possible.

 The Bottom Line

The bottom line is that business enterprises are now focusing on utilizing their data collected from consumers to climb up the ladder of success and make critical strategic decisions based on data-driven insights.

Big Data has opened up a whole new world of possibilities impacting the business landscape in every industry right from SME’s to Fortune 500 companies. Almost all industries like finance, healthcare, education have adopted this technology with IT leading the way. This clearly indicates that big data is moving from an experimental endeavor to a more practical pursuit within the organizations. Not only can businesses improve productivity by shifting from manual to automated processes, but they can also see a significant boost in profitability.

From visualizing consumer behavior to building loyalty and making sound decisions, Big Data Analytics presents itself as a goldmine of opportunities for businesses.

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Eau Claire (WQOW)- President Trump recently expressed his intention to ban Chinese-owned apps, like TikTok and WeChat, from the United States in order to protect Americans from data mining. But how serious is data mining, and what are some ways you can protect yourself from it?

Data mining involves the collection, storage and processing of large sets of data in order to spot patterns. Once identified, these patterns can be used by companies to better understand trends in purchasing behavior, help banks detect fraudulent credit card transactions or even aid in the detection of money laundering or other criminal activities.

However, some argue that the use of data mining is an invasion of customer privacy. Personal information, like social security numbers, may become vulnerable to hacking, identity theft, or other unethical behaviors.

Justin Patchin, an expert in social networking with UW-Eau Claire, said data mining is virtually unavoidable.

“Any website you use, any game you play, social media app, particularly if it’s free, you’re giving up your information by utilizing that platform,” Patchin said.

Patchin said the best way to avoid social media data mining is to avoid social media altogether. Still, other businesses will have your information in one form or another.

When it comes to TikTok, Patchin said users can limit their data sharing by simply viewing videos, rather than posting.

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During the last decade, social networking sites/apps have become the most important channels of communication.Social networks such as Facebook, Twitter, and Instagram contain a considerable amount of informative data not only about social matters but also about business and marketing.

Of course, if you want to take advantage of this ever-changing space to swing the balance in your brand’s favor, you don’t have a choice unless using big data analytics.In this post, I’m going to describe how social media can be affected by big data analytics and how businesses can make the most out of it.

What is big data?

Big data is any novel technique used to analyze a massive volume of data that is so large it is impossible to process with traditional methods.Big data can handle both structured and unstructured data and help you tackle the problem of processing power. The main purpose of using big data techniques is finding overall patterns, trends, and connections between different variables.This is particularly important for studying data related to human interactions and social behaviors. This is exactly where social media can be influenced by big data, especially for marketing purposes.In fact, the huge, unstructured knowledge available on social media can’t add real value to your marketing strategy. So you need a powerful tool like big data analytics to be able to handle it.

Important data on social media

The stream of social posts, likes, mentions, shares, followers, and many other impressions can clearly prove why big data is important in social media marketing.This is now a must for businesses to collect these tones of information in real-time and analyze it to know how well their social presence is.In fact, every single positive interaction can add to their reputation and any negative feedback can put their efforts at stake.Without the shadow of a doubt, social media marketing analytics, especially for big companies, can’t be impactful without big data. Therefore, many businesses are investing masses of money on big data tools to track real-time consumer behavior across social media.To do this you will have to:

1. Collect the most related data available on social networks
2. Recognize the weight of each data on your target market
3. Convert these data into useful facts and use them in your strategies

Of course, the velocity of your data mining, the volume of the data, and also its variety is of paramount.You’ll need to use the best data technology and analytical tools if you want to leverage big data in marketing effectively.Also, it’s important to consider integrating various social networking sites/apps. Using Facebook, Instagram, Twitter, and LinkedIn will lead to better social interactions and the data from them can represent reality more exactly.

Advantages of big data for digital marketing

A lot of big and small companies are thinking about considerable budgets for big data analytics tools to get ahead of the competition.Here are 5 top benefits a big data analysis approach can bring to your social media campaign:1. Taking care of huge information sourcesAs a social user, you may need to process all data related to your niche that comes from mainstream channels.Analyzing diverse channels is not an easy task and can only be done by using artificial intelligence and big data technology.A lot of business sites allow users to sign up via Google or other main channels. So marketers can collect and analyze data about their niche customers from social networks, browser history, applications, cloud storage, etc.2. Targeting the right audienceIt’s clear that you can’t reach out to all the internet users so you have to narrow down to the most probable group of customers.Thus, social media marketing is all about identifying your target audience. Big data technology has provided marketers with access to insightful data of users’ personal information, photos, favorites, locations, and various kinds of activities.3. Predicting online behaviorsA big data approach can also be used for better decision-making based on previous trends. Data-based businesses are becoming incredibly efficient, as computers can predict the potential choices of customers.In sum, the interests and habits of people can be estimated as they’re changing based on specific overall trends.4. Managing marketing campaignsBig data techniques enable marketers to accurately track the ROI metrics of their social media campaigns.It will provide advertisers with insightful data into how effective a social media campaign has been or can be. Predictive analytical methods greatly help in predicting what products/services consumers want.Tracking consumer behaviors all over social will clear many things about the effectiveness of previous campaigns.This includes media including their engagement and reaction to online advertising. So marketers can optimize their plans for future campaigns to get a higher ROI.5. Identifying fair pricesOne of the biggest problems for marketers is to find reasonable prices for sponsored ads. A lot of different factors are affecting the prices.For example, during the COVID-19 pandemic, a lot of influencers have considerably cut their rates. So, it’s important to track the accepted costs on the internet and make arrangements accordingly.A big data analysis on social media can be helpful to clearly know what prices your competitors or niche customers are agreeing at.

Social media strategy when using big data

If you want to make sure that your big data approach will lead to a higher ROI on social media, you need to have an efficient strategy.Without a strategy, you don’t know what exactly you want from the unstructured data on social channels. Therefore, you’ll waste your time and money without generating considerable leads and boosting sales.A common social media strategy usually comprise the below steps:

• Market research
• Defining SMART goals
• Identifying the audience
• Choosing the right platforms
• Generating relevant content
• Scheduling posts and establishing a social presence
• Engaging with the community
• Influencer marketing
• Analyzing the performance

This strategy should help you achieve certain goals which usually contain below items:

• Drive traffic to your site
• Get higher conversions rates
• Build brand awareness
• Make you appear like a niche leader

Additionally, you’ll need certain metrics to analyze your performance and show you how well you’re achieving these goals.Actually, great use of a big data strategy is to help you know your social performance. You’ll be able to optimize your social techniques based on this information and make the best out of it.

Conclusion

Big data is one of the newest features of information technology and can be of great use to digital marketers. It was tried to summarize the important aspects of big data analytics in social media marketing and the benefits it can bring to your campaign. Remember to take advantage of this technology to optimize your social presence and get ahead of the marketing competition.

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Kennesaw State Professor of computer science Dr. Dan Lo and his team of students created a program last semester to data mine Chinese social media sites in partnership with the U.S. Embassy in Beijing, China.

Lo said the program retrieves and deciphers posts on popular Chinese social media outlets like Weibo and WeChat. Lo’s team of student researchers — four undergraduates and two graduates — conducted extensive data mining on China’s Weibo platform and in turn, gained valuable real-world experience under his tutelage.

Weibo, meaning “micro blog” in Chinese, is an open social platform with over 445 million monthly active users. It is similar to both Facebook and Twitter, and likewise is an epicenter of Chinese news.

Lo said he is passionate about the project as it embodies elements of social networking, big data, machine learning and data science. Additionally, it required fluency in both Chinese and Python, a coding language. The team’s biggest challenge was processing Chinese articles correctly because of the complexity of the Chinese language.

“Chinese language is highly context-sensitive. Consecutive Chinese letters can be combined and interpreted in multiple ways.” Lo said. “For instance, our data mining programs needed to determine if ABC was meant to be read as A, B, C, AB, BC or ABC in the mind of the writer. The meaning in each instance varied vastly.”

Handling word segmentation, numbers, acronyms, synonyms and newly developed words in Chinese slang posed additional frustrations. Linguistic challenges and the sheer volume of the big data analyzed required painstaking efforts to codify the inputs.

“Social media mining in English has been performed for many years in Twitter, Facebook, Instagram and other networks,” Lo said. “While it is theoretically possible to do the same in Chinese by applying most of the English techniques, it is not that simple.”

Information sharing on social media is a valuable tool in forming public opinion. The results from the project enabled Lo’s team to show the information shared among Chinese users and the key statistics of that data.

“Dr. Lo is an excellent mentor because he allows his students to have a role in all stages of the research process,” graduate research assistant Charles Gardner said. “By working so closely with his students, Lo creates strong researchers as well as a great finished project.”

Gardner has been working as a graduate research assistant for Lo for over a year now.

“In English, we do not have word segmentation,” Gardner said. “However, in this project, we look at Chinese words within references and it ends up being an interesting system.”

Lo has already begun work on his next exciting project identifying fake news and dealing with misinformation. This new project has been commissioned by the U.S. Embassy in Poland.

The program was awarded to KSU by the The Diplomacy Lab, an alliance between the U.S. Department of State and U.S. colleges and universities, according to KSU News. Under the program, partner U.S. universities are invited to submit proposals for State Department projects in various humanitarian areas.

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As soon as hackers take down your account, you normally get to see suspicious posts that might revolve around deals on products or stuff that you would never like buying online. But how about a situation where hackers plan to infiltrate the account of Facebook’s biggest data partners? Yes, we are going to talk about thousands of dollars and credit cards being stolen in a similar case.

Recently, hackers got access to the personal account of LiveRamp’s employee, only with the aim to get control over the Business Manager’s account and hoping to run scam through the ads with other’s money being spent on them.

By doing so, they successfully attacked one of Facebook’s most prominent data partners, however, the damage was still contained. The incident affected a limited number of LiveRamp customers and associated Ad Accounts, while Facebook actively informed the affected parties about it.

Although LiveRamp didn’t tell the exact number of customers who got affected by the hack and stated that the company has their security measures in place, especially for employees who deal with Facebook ads accounts, but there is one thing for sure that thousands of victim’s dollars were spent into tricking users buy fake products. Facebook, on the other hand, did confirm later in November that personal account of an admin for a Business Manager account but didn’t mention LiveRamp directly.

Nevertheless, LiveRamp and Facebook worked together to cut down unauthorized access and restore the functionality back to normal for its users.

This isn’t the first time that hackers targeted the hub of Facebook’s empire – the advertisers. As advertising has been Facebook’s lifeline for a long period of time — considering how it is expected to add up $84 billion in revenue in 2020 with 2.2 billion users, the social media giant is becoming more and more effective with targeted ads. The company is facilitating businesses from around the world in the best way possible and hackers had to pay attention to their success.

Hence, the bad guys knew that they could scam countless people through the tools that marketers use on the social network.

Why Was LiveRamp Worth It?

Besides being a big data partner for Facebook, LiveRamp is a marketing powerhouse that has earned its name for matching data from the real world actions to online identities, helping advertisers more than their expectations. Thus that is also the reason why LiveRamp is favorite of more than 300 businesses and data providers which includes big names like Google, MasterCard, Uber, Snapchat, Spotify and Equifax.

So LiveRamp for Facebook helps advertisers target ads on the basis of data derived from a user’s offline activities and they also integrated Facebook’s Offline Conversions API to help the same advertisers see the effectiveness of their marketing campaigns with knowing how many people actually bought the product.

Liveramp doesn’t run ads on behalf of Facebook itself but it still has access to do so being a Facebook approved partner. Hence, when hackers ran a series of ads on LiveRamp’s customer accounts on Facebook, one of the ads was viewed more than 60,000 times and further directed users to a page that was made to steal the credit card details of users.

Facebook’s Security

Facebook continuously reminds its users of a number of security tools which primarily includes two-factor authentication and login alerts, just so that one should know if a hacker has tried to intrude. The social network even offers Security Center page for business accounts, along with a recommendation that businesses should go for quarterly security cleanups to make sure that employees don’t have unnecessary access.

However, Facebook only goes with the policy of recommending these security measures and not making it a requirement even for its big partners like LiveRamp which is a big problem actually.

Marcin Kleczynski, CEO of cybersecurity company Malwarebytes raised the concern regarding how Facebook doesn’t require separate Business Manager account and instead users can manage their multi-million dollar pages all through their personal profiles.

He further questioned that why Facebook never opted for higher standards when it comes to bigger partners, especially after knowing how people go for poor security habits including reusing the same password everywhere or not turning on two-factor authentication.

Honestly, till the time Facebook doesn’t make important security measurements a requirement, cybercriminals would have a better chance to have access to million-dollar advertising campaigns all by attacking personal profiles.

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With more computers, internet, social media and tons of coding, there’s a greater chance of glitches occurring, costing businesses millions of dollars and a loss of prestige. With so much data to check and double-check, maybe artificial intelligence (AI) can help stop these “gremlins” from wreaking havoc online.

From Bugs Bunny to Business

Perhaps the most iconic World War II cartoon is the Warner Bros. episode “Falling Hare.” Bugs Bunny pooh-poohs the notion of gremlins committing sabotage on the Allied war effort, until those little creatures cause malfunctions in everything from bombs to planes, with devastating results in the Merrie Melodies classic.

According to Robert O. Harder, in a piece published by MHQ—The Quarterly Journal of Military History, “gremlins” were tall tales told by pilots of mischief makers that would infect aircraft, causing all kinds of maladies. Today, they’re the little glitches that seem harmless enough until they paralyze the titans of the technological age.

In the cartoon, the classic rabbit tries to defeat the gremlins without much success. But what if he had a smart computer to do the job?

Anodot = The Anti-Gremlin Company

To find out more, Observer talked with Ira Cohen, co-founder and chief data scientist of Anodot, a company dedicated to finding anomalies and glitches, like the gremlins that bedeviled warplanes 80 years ago. He must like cartoons, too, because he began our conversation by discussing a South Park episode—the one where Eric Cartman programs his Alexa to do all kinds of bizarre things. In fact, other Alexa devices started doing the same strange things that Cartman’s did. “Good monitoring would show lots of Alexas doing the same thing at the same time. We would catch these kinds of glitches,” he assured me.

How do they do it? “There are two ways our methods are better,” Cohen explained. “First is scale. There’s lots to look at… the sheer quantity of it all. Even small businesses have 100,000 things to check. The other is speed. You need to be able to look at things quickly and be able to find problems faster.”

Believe it or not, smaller companies are just as likely to take it on the chin when they get hit with gremlins. That’s because few of them currently use AI to check their products.

Cohen told a story about a company that had video players and ran a series of ads. Well, the player didn’t work very well with a particular version of Internet Explorer. “Few noticed it at the time. It took several months to discover, and that was done accidentally. It lost lots of ad revenue. Though it looked like a small percentage on paper, it wound up being a big deal in money. You need to look at everything.”

In another case, a cloud provider’s service went down. Anodot figured out what the problem was in 10 minutes and patched in a workaround. Other large companies went down for hours. Finally, the cloud provider revealed the problem, but things weren’t working for a period of time. “When someone else’s company that you rely on for service has a glitch, your customers don’t care about whose fault it is,” Cohen said. “They contract with you, and in their eyes, you didn’t get the job done.”

How about a story where Anodot came to the rescue?

“We found out that scooters that were being ridden were being tossed into the sea because riders in that area were leaving them around, frustrating nearby residents,” Cohen elaborated. “We could figure out pretty quick what was going on, giving the client more time to develop a plan to retrieve them.”

Back in 2014, co-founder and CEO David Drai was working on an Uber competitor for taxis, in charge of business monitoring of all of the variables (service provided, ride quality, money and right destination). He would look at the dashboard in the morning and wonder why service was being constantly interrupted in a place like Moscow. “He faced the pain of late detection and [saw] that an AI solution was needed, because they were always 24 hours too late,” Cohen explained.

And so the decision was made to invest in AI to spot and stop these gremlins, but does Anodot’s product exceed what a human can do? “We have an 80% reduction in error detection time, and we’ve done a webinar that shares the details of that head-to-head test. In fact, customers measure us as to how fast we can find something,” Cohen shared.

The Gremlin Finder’s Future, and Past
How does one start preparing for a future in catching computer glitches? Cohen began in electrical engineering in college, moving to image processing and computer visual practices, “a slippery slope to AI.” His first job was catching errors in computer chips associated with the production process.

But the lightbulb went off years earlier. In a tale of a fourth grade programmer, Cohen created an adventure game on an Apple II, programming in Basic. “It wasn’t about playing games but getting the computer to do what I wanted it to do—that was my hook,” Cohen elaborated. “So this is what all that math is for. It wasn’t just problems given by the teacher, but getting a machine to do a task. It made the abstract math real. Now, we find the defects that can cost a company a lot of money.”

So what can we expect over the next 10 years? “BI, or business intelligence, will gradually be replaced by AI. Visualization tools, dashboards, reporting, helping companies make better and quicker decisions will be the next step,” Cohen concluded. “There’s plenty of data and graphs that can be created. But without asking the right questions, you won’t get the right answers. AI will get involved in preparing us for the questions, even the ones we haven’t considered. More decisions will be automated, moving us away from a labor-intensive process.”

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