Disinformation is rampant on social media – a social psychologist explains the tactics used against you

H. Colleen Sinclair, Louisiana State University

Information warfare abounds, and everyone online has been drafted whether they know it or not.

Disinformation is deliberately generated misleading content disseminated for selfish or malicious purposes. Unlike misinformation, which may be shared unwittingly or with good intentions, disinformation aims to foment distrust, destabilize institutions, discredit good intentions, defame opponents and delegitimize sources of knowledge such as science and journalism.

Many governments engage in disinformation campaigns. For instance, the Russian government has used images of celebrities to attract attention to anti-Ukraine propaganda. Meta, parent company of Facebook and Instagram, warned on Nov. 30, 2023, that China has stepped up its disinformation operations.

Disinformation is nothing new, and information warfare has been practiced by many countries, including the U.S. But the internet gives disinformation campaigns unprecedented reach. Foreign governments, internet trolls, domestic and international extremists, opportunistic profiteers and even paid disinformation agencies exploit the internet to spread questionable content. Periods of civil unrest, natural disasters, health crises and wars trigger anxiety and the hunt for information, which disinformation agents take advantage of.

Certainly it's worth watching for the warning signs for misinformation and dangerous speech, but there are additional tactics disinformation agents employ.

It's just a joke

Hahaganda is a tactic in which disinformation agents use memes, political comedy from state-run outlets, or speeches to make light of serious matters, attack others, minimize violence or dehumanize, and deflect blame.

This approach provides an easy defense: If challenged, the disinformation agents can say, “Can't you take a joke?” often followed by accusations of being too politically correct.

Shhh … tell everyone

Rumor-milling is a tactic in which the disinformation agents claim to have exclusive access to secrets they allege are being purposefully concealed. They indicate that you will “only hear this here” and will imply that others are unwilling to share the alleged truth – for example, “The media won't report this” or “The government doesn't want you to know” and “I shouldn't be telling you this … .”

But they do not insist that the information be kept secret, and will instead include encouragement to share it – for example, “Make this go viral” or “Most people won't have the courage to share this.” It's important to question how an author or speaker could have come by such “secret” information and what their motive is to prompt you to share it.

People are saying

Often disinformation has no real evidence, so instead disinformation agents will find or make up people to support their assertions. This impersonation can take multiple forms. Disinformation agents will use anecdotes as evidence, especially sympathetic stories from vulnerable groups such as women or children.

Similarly, they may disseminate “concerned citizens'” perspectives. These layperson experts present their social identity as providing the authority to speak on a matter; “As a mother …,” “As a veteran …,” “As a police officer ….” Convert communicators, or people who allegedly change from the “wrong” position to the “right” one, can be especially persuasive, such as the woman who got an abortion but regretted it. These people often don't actually exist or may be coerced or paid.

If ordinary people don't suffice, fake experts may be used. Some are fabricated, and you can watch out for “inauthentic user” behavior, for example, by checking X – formerly Twitter – accounts using the Botometer. But fake experts can come in different varieties.

• A faux expert is someone used for their title but doesn't have actual relevant expertise.
• A pseudoexpert is someone who claims relevant expertise but has no actual training.
• A junk expert is a sellout. They may have had expertise once but now say whatever is profitable. You can often find these people have supported other dubious claims – for example, that smoking doesn't cause cancer – or work for institutes that regularly produce questionable “scholarship.”
• An echo expert is when disinformation sources cite each other to provide credence for their claims. China and Russia routinely cite one another's newspapers.
• A stolen expert is someone who exists, but they weren't actually contacted and their research is misinterpreted. Likewise, disinformation agents also steal credibility from known news sources, such as by typosquatting, the practice of setting up a domain name that closely resembles a legitimate organization's.

You can check whether accounts, anecdotal or scientific, have been verified by other reliable sources. Google the name. Check expertise status, source validity and interpretation of research. Remember, one story or interpretation is not necessarily representative.

It's all a conspiracy

Conspiratorial narratives involve some malevolent force – for example, “the deep state,” – engaged in covert actions with the aim to cause harm to society. That certain conspiracies such as MK-Ultra and Watergate have been confirmed is often offered as evidence for the validity of new unfounded conspiracies.

Nonetheless, disinformation agents find that constructing a conspiracy is an effective means to remind people of past reasons to distrust governments, scientists or other trustworthy sources.

But extraordinary claims require extraordinary evidence. Remember, the conspiracies that were ultimately unveiled had evidence – often from sources like investigative journalists, scientists and government investigations. Be particularly wary of conspiracies that try to delegitimize knowledge-producing institutions like universities, research labs, government agencies and news outlets by claiming that they are in on a cover-up.

Good vs. evil

Disinformation often serves the dual purpose of making the originator look good and their opponents look bad. Disinformation takes this further by painting issues as a battle between good and evil, using accusations of evilness to legitimize violence. Russia is particularly fond of accusing others of being secret Nazis, pedophiles or Satanists. Meanwhile, they often depict their soldiers as helping children and the elderly.

Be especially wary of accusations of atrocities like genocide, especially under the attention-grabbing “breaking news” headline. Accusations abound. Verify the facts and how the information was obtained.

Are you with us or against us?

A false dichotomy narrative sets up the reader to believe that they have one of two mutually exclusive options; a good or a bad one, a right or a wrong one, a red pill or a blue pill. You can accept their version of reality or be an idiot or “sheeple.”

There are always more options than those being presented, and issues are rarely so black and white. This is just one of the tactics in brigading, where disinformation agents seek to silence dissenting viewpoints by casting them as the wrong choice.

Turning the tables

Whataboutism is a classic Russian disinformation technique they use to deflect attention from their own wrongdoings by alleging the wrongdoings of others. These allegations about the actions of others may be true or false but are nonetheless irrelevant to the matter at hand. The potential past wrongs of one group does not mean you should ignore the current wrongs of another.

Disinformation agents also often cast their group as the wronged party. They only engage in disinformation because their “enemy” engages in disinformation against them; they only attack to defend; and their reaction was appropriate, while that of others was an overreaction. This type of competitive victimhood is particularly pervasive when groups have been embedded in a long-lasting conflict.

In all of these cases, the disinformation agent is aware that they are deflecting, misleading, trolling or outright fabricating. If you don't believe them, they at least want to make you question what, if anything, you can believe.

You often look into the things you buy rather than taking the advertising at face value before you hand over your money. This should also go for what information you buy into.

H. Colleen Sinclair, Associate Research Professor of Social Psychology, Louisiana State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

When research study materials don't speak their participants' language, data can get lost in translation

Sonia Colina, University of Arizona

Imagine your mother has cancer. You just heard about a promising new experimental treatment and want to enroll her in the study. However, your mother immigrated to the U.S. as an adult and speaks limited English. When you reach out to the research team, they tell you she is ineligible because they are recruiting only English speakers.

Unfortunately, this is an all too likely outcome of a scenario like this, because non-English speakers are frequently excluded from clinical trials and research studies in the U.S.

Despite efforts to increase research participation, racial and ethnic minorities remain underrepresented in results. A review of 5,008 papers in three pediatric journals from 2012 to 2021 revealed that only 9% of these studies included non-English speaking volunteers.

Language is a key barrier to participation, as even those with some English proficiency are less likely to participate in studies when recruitment materials aren't in their native language. Language barriers also hinder a person's ability to provide informed consent to participate.

This problem is not likely to fade away. The number of people with limited English proficiency in the U.S. grew by 80% between 1990 and 2013, going from nearly 14 million to 25.1 million people. As of 2022, this number rose to 26.5 million people. Excluding people with limited English proficiency is not only unethical, as these groups deserve the same access to experimental and evolving therapies as the English-speaking population, but also limits how applicable research findings are to the general population.

One way to eliminate language barriers is by translating research documents. As a translation scholar, I strive to discover ways to improve translation quality to benefit the research community and broader society. Translation in research, however, is not straightforward. Not only must the translated materials be accurate, they also have to serve their intended purpose.

The most commonly used method to evaluate translation quality in health research is backtranslation – translating the translated text into the original language and assessing how well it matches the original text. And yet, this method relies on outdated scholarship from the 1970s, perpetuating serious misconceptions about how translation works.

Understanding translation

Translation involves much more than just transferring written words from one language to another. For many health researchers, the goal of translation is to transfer meaning so it remains intact in a new language. Along these lines, the translator is meant to be a conduit of perfect linguistic equivalence. Yet, current work in the field of translation studies indicates this perfect match or meaning transfer is only an illusion.

A translator is not a conduit of meaning, but both a reader of the original text and a writer of the translation. As such, translators have their own positioning in the world that comes with a set of conscious or unconscious values and knowledge that bias how they read and write. Translation is a process of interpretation regardless of how objective a translator aims to be.

Furthermore, languages do not match structurally or culturally. For instance, the English sentence “I arrived late” structurally corresponds to the Spanish “Yo llegué tarde” because the grammar lines up. But because Spanish expresses subject information through verb endings (“lleg-ué”), the “Yo” is normally interpreted as contrastive, meaning that “I” was the one who arrived late as opposed to someone else.

A perfect match in backtranslation often reflects a translation that is too similar to the original, such that it often contradicts the norms of the translated language. For instance, a health status questionnaire translated “My thinking is clear” into Portuguese as “O meu pensamento é claro.” Despite good backtranslation results, patients in Brazil stated it was unclear. Changing it to “Consigo pensar claramente” (“I am able to think clearly”) communicated more effectively and naturally with the target population.

Translation scholars suggest that a more realistic, descriptive and explanatory approach to translation is one governed by what the commissioner wants to achieve with the translation. Under this view, the translator makes decisions according to the type of text being translated and to the purpose of the translation.

How translators approach texts and what strategies they use to translate them varies with each document. Some texts require closer adherence to the words of the original than others. For instance, legal or regulatory considerations require translating the chemical ingredients list of a medication more closely to the structure of the source than a recruitment flyer that aims to convince readers to participate in a study. Translators of research documents need to determine the needs of the specific text in collaboration with both the researchers and representatives of the population they're studying.

Translation affects research results

Recent studies show that translation can affect data validity and reliability. An inadequate approach may result in translated materials that don't work as intended. For instance, a survey may produce incomplete or incorrect data if participants misunderstand or are unclear about the questions.

My team and I investigated how different translation approaches affected how readers responded to translated materials. We had bilingual participants review two versions of a survey measuring perceptions of stress. Each version was translated into Spanish in a different way.

One of the two translations was produced with a literal approach that aimed to be as equivalent as possible to the original, while the other followed a functionalist approach that focused on achieving the purpose specified for the translation. In this case, the goal was to obtain data on how a Spanish-speaking population perceives daily stress.

We asked participants to review the two translated versions of the survey, then indicate any unclear sections and which version they preferred. We found that participants preferred the functionalist translation and identified a higher number of problems in the translation focused on equivalence. Participants commented that the “equivalent” translation was more difficult to understand, too direct and seemed obviously translated.

Other studies have shown that translated materials are less accessible overall compared with the original documents. Researchers have also found that some translation approaches increase reading complexity. One study found that a survey used to measure the health progress of patients translated with a functionalist approach had better readability than published counterparts that used a more literal approach.

The translation process is complicated. Lack of awareness of its complexities can affect not only equitable participation in research but also the validity and reliability of its methodology and findings. But with the right approach, translation can increase a study's reach, diversify its data and lead to new findings and ideas. Reaching out to a translation scholar before starting a project can help scholars prevent their data and research from getting lost in translation.

Sonia Colina, Professor of Spanish and Portuguese, University of Arizona

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Your car might be watching you to keep you safe − at the expense of your privacy

M. Hadi Amini, Florida International University

Depending on which late-model vehicle you own, your car might be watching you – literally and figuratively – as you drive down the road. It's watching you with cameras that monitor the cabin and track where you're looking, and with sensors that track your speed, lane position and rate of acceleration.

Your car uses this data to make your ride safe, comfortable and convenient. For example, the cameras can tell when you've been distracted and need to bring your attention back to the road. They can also identify when you are speeding by verifying the speed limit from your GPS position or traffic signs along the road and warn you to slow down. Some carmakers are also beginning to incorporate similar features for convenience, such as unlocking your car by scanning your face or fingerprint. Your car may also transmit some of this data to the manufacturer's data centers, where the company uses it to improve your driving experience or provide you with personalized services.

In addition to providing these benefits, this data collection is a potential privacy nightmare. The information can reveal your identity, your habits when you're in your car, how safely you drive, where you've been and where you regularly go. A report by the Mozilla Foundation, a nonprofit technology research and advocacy organization, found that carmakers' privacy policies are exceedingly lax. The study identified cars as the “worst category of products for privacy that we have ever reviewed.” U.S. Sen. Ed Markey wrote a letter to U.S. automakers on Nov. 30, 2023, asking a lengthy set of questions about their data practices.

Today's smart cars present drivers with a trade-off between convenience and privacy, assuming drivers have the option of improving the data privacy of their cars. As a computer scientist who studies cybersecurity and resilience in transportation, I see several technological routes to getting the best of both worlds: cars that make use of this collected data while also preserving users' privacy.

Driver data

Today's cars use a wide range of sensors to understand the environment, analyze the data and ensure the safety of passengers. For instance, cars are equipped with sensors that measure brake pedal position, vehicle speed, driver's movements, surrounding vehicles and even traffic lights. The collected data is transmitted to the car's electric control units, the computers that operate the car's many systems.

There are two types of sensors that continuously monitor and predict a driver's drowsiness. The first is vehicle status monitoring sensors such as lane detection and steering wheel position tracking. This data is not directly related to a specific person and can be considered not personally identifiable information unless it is correlated with other data that identifies the driver.

The second type of sensors tracks drivers themselves. This category includes things like cameras to track the driver's eye movements to predict fatigue. This second group of sensors is directly related to the driver's privacy because they collect personally identifiable information, such as the driver's face.

Protecting privacy

There is a trade-off between the quality of the driving experience and the privacy of drivers, depending on the level of services and features. Some drivers may prefer to share their biometric data to facilitate accessing a car's functions and automating a major part of their driving experience. Others may prefer to manually control the car's systems, sharing less personally identifiable information or none at all.

At first glance, it seems the trade-off of privacy and driver comfort cannot be avoided. Car manufacturers tend to take measures to protect drivers' data against data thieves, but they collect a lot of data themselves. And as the Mozilla Foundation report showed, most car companies reserve the right to sell your data. Researchers are working on developing data analytics tools that better protect privacy and make progress on eliminating the trade-off.

For instance, over the past seven years, the concept of federated machine learning has attracted attention because it allows algorithms to learn from the data on your local device without copying the data to a central server. For instance, Google's Gboard keyboard benefits from federated learning to better guess the next word you are likely to type without sharing your private data with a server.

Research led by Ervin Moore, a Ph.D. student at Florida International University's Sustainability, Optimization, and Learning for InterDependent Networks laboratory, and published in IEEE Internet of Things Journal explored the idea of using blockchain-based federated machine learning to improve the privacy and security of users and their sensitive data. The technique could be used to protect drivers' data. There are other techniques to preserve privacy as well, such as location obfuscation, which alters the user's location data to prevent their location from being revealed.

While there is still a trade-off between user privacy and quality of service, privacy-preserving data analytics techniques could pave the way for using data without leaking drivers' and passengers' personally identifiable information. This way, drivers could benefit from a wide range of modern cars' services and features without paying the high cost of lost privacy.

M. Hadi Amini, Assistant Professor of Computing and Information Sciences, Florida International University

This article is republished from The Conversation under a Creative Commons license. Read the original article.