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Guess which states saw the most election disinformation in 2020

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On November 3, Tina Barton ran into a problem. It was Election Day in the US and Barton, a Republican, was city clerk for Rochester Hills, Michigan, a conservative-leaning community near Detroit. As she was uploading some of the voting results, there was a technical issue, which she reported to Oakland County officials. But the voting data wasn’t fixed for two days—by which time the entire country was looking at the state’s election results. 

The change was very, very public, and it generated a huge swell of misinformation. This was supercharged on November 6, when Ronna McDaniel, the chair of the Republican National Committee, flew to Oakland County and held a press conference. She claimed that 2,000 ballots had been counted as Republican before being “given” to Democrats. 

“If we are going to come out of this and say this was a fair and free election, what we are hearing from the city of Detroit is deeply troubling,” McDaniel said.

Upset at how the situation was being misrepresented, Barton posted a video on Twitter refuting the claims. She’s been the Rochester Hills clerk for eight years, and when she spoke out against McDaniel, she knew she was putting her career on the line. In the video, which has since been deleted, Barton said, “I am disturbed that this is intentionally being mischaracterized to undermine the election process.” 

Her remarks went viral, and they were met with threats and anger. In an email to MIT Technology Review, Barton said that “since Ms. McDaniel’s press conference, I have received threatening voice mails and messages.” One caller claimed to be on the way to Michigan. Barton upgraded the security system of her home.

Targeting our natural fears

Data shows that during the election, disinformation was highly targeted locally, with voters in swing states exposed to significantly more online messages about voter intimidation, fraud, ballot glitches, and unrest than voters in other states. 

In a data set provided by Zignal Labs, we looked at mentions of over 30 terms related to voter suppression or intimidation, fraud, technical errors, and unrest that focused on a particular polling location. Our sample of 16 states found that between October 1 and November 13, swing states had more than four times the amount of localized voting misinformation: a median of 115,200 such mentions, while non-swing states saw a median of 28,000 related messages.

Here’s a chart showing how the volume of messages changed over the days before the election itself.

Mentions relating to voter intimidation, fraud, technical glitches, and voter suppression at specific polling places

Bhaskar Chakravorti, dean of global business at Tuft University’s Fletcher School, conducts research on the conditions that leave a community particularly vulnerable to disinformation. He says that this local focus is typical of effective disinformation campaigns, which are usually pinned to a specific place and slice the target audience into its smallest, stereotyped parts. “Clever misinformation” is organized, he says, in the same way that political campaigning is. 

Disinformation is “targeted at our natural or native hopes and fears, and hopes and fears vary depending on who I am,” he says. “It varies depending on how rich or poor I am. It varies depending on what my ethnicity or race is.” 

In some places, this localization was more visible than in others. In Florida, Latino voters were subjected to intense campaigns based on their age, heritage, or neighborhood profiles as both parties fought to win the state. As a result of being flooded with this material, says Chakravorti, voters grew distrustful of political information at large and turned to more private spaces for discourse—which were, in fact, ripe environments for localized disinformation that became particularly hard to confront. 

Two-pronged approach

These problems all came despite the fact that election officials were significantly better prepared for the challenges in 2020 than in the previous presidential election. Many secretaries of state conducted media blitzes intended to direct people to trusted sources of information for voting, while also battling specific rumors. 

Elizabeth Howard, senior counsel at the Brennan Center for Justice, describes it as a two-pronged approach. It involved “proactively educating voters about what’s going on,” she says, “and then, to varying degrees, election officials working to identify and combat mis- and disinformation at the local and hyper-local level.” 

Despite all their efforts, however, disinformation about polling still wreaked havoc—particularly for election officials like Tina Barton, who, says Howard, “are just doing their job in compliance with state law across the country.” 

Chakravorti says fighting this disinformation in the future may require the use of small-scale media campaigns, local influencers, and community-level ads that spread trusted content. But these tactics won’t fix the deeper structural issues that make a community vulnerable to disinformation. Chakravorti found, unsurprisingly, that some key indicators of vulnerability for US states include political competitiveness, education levels, polarization, and degree of trust in news sources. And none of those issues are new.

Lights out

In September 1993, the FBI sent a safety alert to the Chicago police department warning of a rumored “new and murderous initiation ritual” for the city’s most notorious street gang. The supposed ceremony required prospective members to drive at night with their headlights off to lure and kill unsuspecting drivers. The claim turned out to be false—but the rumor spread like wildfire. 

According to researchers who have studied the “lights out” urban legend, it flourished partly because the summer of 1993 was one of the worst stretches of crime Chicago had ever seen. Tensions were high, seeded by deep-rooted racial friction and political polarization. 

Disinformation—whether it’s gang folklore or rumors of election intimidation—is almost always most effective at a local level. It’s worse in polarized, closed environments. We’re most likely to believe things from our own circle. We still struggle to dispel the neighborhood rumor mill, and we certainly don’t know how to do it at scale. 

And while the struggle to fight disinformation continues, local officials like Tina Barton are under increasing pressure.

“These are things that take a huge personal toll on our election officials,” says the Brennan Center’s Howard. “These are big stakes for people. These are their neighbors. These are their friends.”

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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MealMe raises $900,000 for its food search engine

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This morning MealMe.ai, a food search engine, announced that it has closed a $900,000 pre-seed round. Palm Drive Capital led the round, with participation from Slow Ventures and CP Ventures.

TechCrunch first became familiar with MealMe when it presented as part of the Techstars Atlanta demo day last October, mentioning it in a roundup of favorite startups from a group of the accelerator’s startup cohorts.

The company’s product allows users to search for food, or a restaurant. It then displays price points from various food-delivery apps for what the user wants to eat and have delivered. And, notably, MealMe allows for in-app checkout, regardless of the selected provider.

The service could boost pricing and delivery-speed transparency amongst the different apps that help folks eat, like DoorDash and Uber Eats. But Mealme didn’t start out looking to build a search engine. Instead it took a few changes in direction to get there.

From social network to search engine

MealMe is an example of a startup whose first idea proved only directionally correct. The company began life as a food-focused social network, co-founder Matthew Bouchner told TechCrunch. That iteration of the service allowed users to view posted food pictures, and then find ordering options for what they saw.

While still operating as a social network, MealMe applied to both Y Combinator and Techstars, but wasn’t accepted at either.

The startup discovered that some of its users were posting food pics simply to get the service to tell them which delivery services would be able to bring them what they wanted. From that learning the company focused on building a food search engine, allowing users to search for restaurants, and then vet various delivery options and prices. That iteration of the product got the company into Techstars Atlanta, eventually leading to the demo day that TechCrunch reviewed.

During its time in Techstars, the company adjusted its model to not merely link to DoorDash and others, but to handle checkout inside of its own application. This captures more gross merchandize value (GMV) inside of MealMe, Bouchner explained in an interview. The capability was rolled out in September of 2020.

Since then the company has seen rapid growth, which it measures at around 20% week-on-week. During TechCrunch’s interview with MealMe, the company said that it had reached a GMV run rate of more than $500,000, and was scaling toward the $1 million mark. In the intervening weeks the company passed the $1 million GMV run-rate threshold.

MealMe was slightly coy on its business model, but it appears to make margin between what it charges users for orders and the total revenue it passes along to food delivery apps.

TechCrunch was curious about platform risk at MealMe; could the company get away with offering price comparison and ordering across multiple third-party delivery services without raising the ire of the companies behind those apps? At the time of our interview, Bouchner said that his company had not seen pushback from the services it sends users to. His company’s goal is to grow quickly, become a useful revenue source for the DoorDashes of the world, and then reach out for some of formal agreement, he explained.

“We continue to be a powerful revenue generator and drive thousands of orders to food delivery services per week,” the co-founder said in a written statement. Certainly MealMe found investors more excited by its growth than concerned about Uber Eats or other apps cutting the startup off from their service.

What first caught my eye about MealMe was the realization of how much I would have used it in my early 20s. Perhaps the company can find enough users like my younger self to help it scale to sufficient size that it can go to the major food ordering companies and demand a cut, not merely avoid being cut off.

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Apple supplier Foxconn reaches tentative agreement to build Fisker’s next electric car

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Apple supplier Foxconn Technology Group has reached a tentative agreement with electric vehicle startup-turned-SPAC Fisker to develop and eventually manufacture an EV that will be sold in North America, Europe, China and India.

Fisker and Foxconn said Wednesday that a memorandum of understanding agreement has been signed. Discussions between the two companies will continue with the expectation that a formal partnership agreement will be reached during the second quarter of this year. 

Under the agreement, Foxconn will begin production in the fourth quarter of 2023 with a projected annual volume of more than 250,000 vehicles. The electric vehicle will carry the Fisker brand.

Foxconn Technology Group Chairman Young-way Liu touted the company’s vertically integrated global supply chain and accumulated engineering capabilities, noting that it gives the company two major advantages in the development and manufacturing of the key elements of an EV, which includes the electric motor, electric control module and battery.

That supply chain and ability to scale engineering quickly will be critical for Foxconn if it hopes to meet its production target.

“The collaboration between our firms means that it will only take 24 months to produce the next Fisker vehicle — from research and development to production, reducing half of the traditional time required to bring a new vehicle to market,” Young-way Liu said in a statement.

Fisker said production of the Ocean SUV — its first EV and one that is supposed to be built by contract manufacturer Magna — will begin in the fourth quarter of 2022. The company said it plans to unveil a production-intent prototype of the Ocean later this year.

This is not Foxconn’s first foray into electric vehicle manufacturing.

Foxconn announced in January 2020 that it had formed a joint venture with Fiat Chrysler Automobiles to build electric vehicles in China. Under that agreement, each party will own 50% of the venture to develop and manufacture electric vehicles and engage in an IOV, what Foxconn parent company Hon Hai calls the “internet of vehicles” business.

Last month, Foxconn and Chinese automaker Zhejiang Geely Holding Group agreed to form a joint venture focused on contract manufacturing for automakers, with a specific focus on electrification, connectivity and autonomous driving technology as well as vehicles designed for sharing.

The joint venture between Foxconn and Geely will provide consulting services on whole vehicles, parts, intelligent drive systems and other automotive ecosystem platforms to automakers as well as ridesharing companies. Geely said it will bring its experience in the automotive fields of design, engineering, R&D, intelligent manufacturing, supply chain management and quality control while Foxconn will bring its manufacturing and Information and Communication Technology (ICT) know-how.

 

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Select Star raises seed to automatically document datasets for data scientists

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Back when I was a wee lad with a very security-compromised MySQL installation, I used to answer every web request with multiple “SELECT *” database requests — give me all the data and I’ll figure out what to do with it myself.

Today in a modern, data-intensive org, “SELECT *” will kill you. With petabytes of information, tens of thousands of tables (on the small side!), and millions and perhaps billions of calls flung at the database server, data science teams can no longer just ask for all the data and start working with it immediately.

Big data has led to the rise of data warehouses and data lakes (and apparently data lake houses), infrastructure to make accessing data more robust and easy. There is still a cataloguing and discovery problem though — just because you have all of your data in one place doesn’t mean a data scientist knows what the data represents, who owns it, or what that data might affect in the myriad of web and corporate reporting apps built on top of it.

That’s where Select Star comes in. The startup, which was founded about a year ago in March 2020, is designed to automatically build out metadata within the context of a data warehouse. From there, it offers a full-text search that allows users to quickly find data as well as “heat map” signals in its search results which can quickly pinpoint which columns of a dataset are most used by applications within a company and have the most queries that reference them.

The product is SaaS, and it is designed to allow for quick onboarding by connecting to a customer’s data warehouse or business intelligence (BI) tool.

Select Star’s interface allows data scientists to understand what data they are looking at. Photo via Select Star.

Shinji Kim, the sole founder and CEO, explained that the tool is a solution to a problem she has seen directly in corporate data science teams. She formerly founded Concord Systems, a real-time data processing startup that was acquired by Akamai in 2016. “The part that I noticed is that we now have all the data and we have the ability to compute, but now the next challenge is to know what the data is and how to use it,” she explained.

She said that “tribal knowledge is starting to become more wasteful [in] time and pain in growing companies” and pointed out that large companies like Facebook, Airbnb, Uber, Lyft, Spotify and others have built out their own homebrewed data discovery tools. Her mission for Select Star is to allow any corporation to quickly tap into an easy-to-use platform to solve this problem.

The company raised a $2.5 million seed round led by Bowery Capital with participation from Background Capital and a number of prominent angels including Spencer Kimball, Scott Belsky, Nick Caldwell, Michael Li, Ryan Denehy and TLC Collective.

Data discovery tools have been around in some form for years, with popular companies like Alation having raised tens of millions of VC dollars over the years. Kim sees an opportunity to compete by offering a better onboarding experience and also automating large parts of the workflow that remain manual for many alternative data discovery tools. With many of these tools, “they don’t do the work of connecting and building the relationship,” between data she said, adding that “documentation is still important, but being able to automatically generate [metadata] allows data teams to get value right away.”

Select Star’s team, with CEO and founder Shinji Kim in top row, middle. Photo via Select Star.

In addition to just understanding data, Select Star can help data engineers begin to figure out how to change their databases without leading to cascading errors. The platform can identify how columns are used and how a change to one may affect other applications or even other datasets.

Select Star is coming out of private beta today. The company’s team currently has seven people, and Kim says they are focused on growing the team and making it even easier to onboard users by the end of the year.

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