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Published on 25 September 2022 by Shona McCombes . Revised on 12 May 2023.
Summarising , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.
There are five key steps that can help you to write a summary:
Writing a summary does not involve critiquing or analysing the source. You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).
Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.
When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, frequently asked questions.
There are many situations in which you might have to summarise an article or other source:
When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.
But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyse or critique it.
In any case, the goal of summarising is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.
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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:
There are some tricks you can use to identify the key points as you read:
To make the text more manageable and understand its sub-points, break it down into smaller sections.
If the text is a scientific paper that follows a standard empirical structure, it is probably already organised into clearly marked sections, usually including an introduction, methods, results, and discussion.
Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.
Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?
Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.
In a scientific article, there are some easy questions you can ask to identify the key points in each part.
Introduction | or problem was addressed? formulated? |
---|---|
Methods | |
Results | |
Discussion/conclusion |
If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.
In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.
Now that you know the key points that the article aims to communicate, you need to put them in your own words.
To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.
The best way to do this is to put the article aside and write out your own understanding of the author’s key points.
Let’s take a look at an example. Below, we summarise this article , which scientifically investigates the old saying ‘an apple a day keeps the doctor away’.
An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.
For example, in a literature review or research paper, you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.
When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.
You can easily create your citations and references in APA or MLA using our free citation generators.
APA Citation Generator MLA Citation Generator
Finally, read through the article once more to ensure that:
If you’re summarising many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.
A summary is a short overview of the main points of an article or other source, written entirely in your own words.
Save yourself some time with the free summariser.
A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarising, and on the purpose of the summary.
With the summariser tool you can easily adjust the length of your summary.
You might have to write a summary of a source:
To avoid plagiarism when summarising an article or other source, follow these two rules:
An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarise the whole text, not just introduce it.
An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarise a source in a paper , in a literature review , or as a standalone assignment.
If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.
McCombes, S. (2023, May 12). How to Write a Summary | Guide & Examples. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/working-sources/how-to-write-a-summary/
Other students also liked, how to paraphrase | step-by-step guide & examples, how to quote | citing quotes in harvard & apa, apa referencing (7th ed.) quick guide | in-text citations & references.
Vice President and Democratic presidential candidate Kamala Harris and former President and Republican presidential candidate Donald Trump speak during a presidential debate. Saul Loeb/AFP via Getty Images hide caption
Vice President Harris and former President Donald Trump faced off Tuesday in their first — and possibly only — debate of the 2024 campaign, taking questions on key issues like the border, the economy and abortion.
With the candidates virtually tied in the polls, and just 55 days until Election Day, Trump and Harris sought to define their visions for America in front of a national audience and deflect attacks from the other side.
NPR reporters fact-checked the candidates' claims in real time . Here's what they found:
TRUMP: "I had no inflation, virtually no inflation. They had the highest inflation, perhaps in the history of our country, because I've never seen a worse period of time. People can't go out and buy cereal or bacon or eggs or anything else."
Inflation soared to a four-decade high of 9.1% in 2022, according to the consumer price index. While inflation has since fallen to 2.9% (as of July), prices — particularly food prices — are still higher than many Americans would like.
Other countries have also faced high inflation in the wake of the pandemic, as tangled supply chains struggled to keep pace with surging demand. Russia’s invasion of Ukraine also fueled inflation by driving up energy and food prices worldwide.
Government spending in the U.S. under both the Biden-Harris administration and Trump also may have contributed, putting more money in people’s pockets and enabling them to keep spending in the face of high prices.
While high prices are a source of frustration for many Americans, the average worker has more buying power today than she did before the pandemic. Since February 2020 (just before the pandemic took hold in the U.S.), consumer prices have risen 21.6% while average wages have risen 23%.
Many prices were depressed early in the pandemic, however, so the comparison is less flattering if you start the clock when President Biden and Vice President Harris took office. Since early 2021, consumer prices have risen 19.6%, while average wages have risen 16.9%. Wage gains have been outpacing price increases for over a year, so that gap should eventually close.
— NPR economics correspondent Scott Horsley
Taylor swift endorses kamala harris in instagram post after the debate.
HARRIS: "Donald Trump left us the worst unemployment since the Great Depression."
At the height of the Great Depression in 1933, the national unemployment rate was near 25%, according to the Franklin D. Roosevelt Presidential Library.
At the start of the COVID pandemic, the unemployment rate peaked at 14.8% in April 2020, a level not seen since 1948, according to the Congressional Research Service.
But by the time Trump left office, unemployment had fallen to a lower, but still elevated, level. The January 2021 unemployment rate was 6.3%.
— NPR producer Lexie Schapitl
TRUMP: "You see what's happening with towns throughout the United States. You look at Springfield, Ohio, you look at Aurora in Colorado. They are taking over the towns. They're taking over buildings. They're going in violently. These are the people that she and Biden let into our country, and they're destroying our country. They're dangerous. They're at the highest level of criminality, and we have to get them out."
Trump attacked Harris and Biden's records on immigration, arguing that they're failing to stem people from other countries from entering the U.S. and causing violence.
In the last two years, more than 40,000 Venezuelan immigrants have arrived in the Denver metro area. And it is true that many now live in Aurora.
A few weeks ago, a video of gang members in an Aurora, Colo., apartment building had right-wing media declaring the city's takeover by Venezuelan gangs. NPR looked into these claims .
Shortly after the video appeared, Colorado's Republican Party sent a fundraising letter claiming the state is under violent attack, and Venezuelan gangs have taken over Aurora.
It's also true Aurora police have recently arrested 10 members of a Venezuelan gang called Tren de Aragua. But Aurora's interim police chief, Heather Morris, says there's no evidence of a gang takeover of apartment buildings in her city.
What's more, violent crime — including murder, robbery and rape — is way down nationwide, according to the most recent data from the FBI . Notably, analysts predict violent crime rates this year will fall back down to where they were before they surged during the pandemic and may even approach a 50-year low.
Trump also claims that migrants are driving up crime rates in the U.S. That is not true. Researchers from Stanford University found that since the 1960s, immigrants have been 60% less likely to be incarcerated than people born in the U.S. The Cato Institute, a libertarian think tank, found undocumented immigrants in Texas were 37% less likely to be convicted of a crime.
— NPR immigration correspondent Jasmine Garsd and criminal justice reporter Meg Anderson
TRUMP: "In Springfield, they're eating the dogs. The people that came in, they're eating the cats. They're eating the pets of the people that live there."
This remark refers to a debunked, dehumanizing claim that Haitian migrants living in Springfield, Ohio, are abducting pets and eating them .
Jd vance spreads debunked claims about haitian immigrants eating pets.
The claim, which local police say is baseless, first circulated among far-right activists, local Republicans and neo-Nazis before being picked up by congressional leaders, vice presidential candidate JD Vance and others. A well-known advocate for the Haitian community says she received a wave of racist harassment after Vance shared the theory on social media.
The Springfield News-Sun reported that local police said that incidents of pets being stolen or eaten were "not something that's on our radar right now." The paper said the unsubstantiated claim seems to have started with a post in a Springfield Facebook group that was widely shared across social media.
The claim is the latest example of Trump leaning into anti-immigrant rhetoric. Since entering the political arena in 2015, Trump accused immigrants of being criminals, rapists, or "poisoning the blood of our nation."
— NPR immigration correspondent Jasmine Garsd
TRUMP: "A lot of these illegal immigrants coming in, [Democrats] are trying to get them to vote."
It is illegal for noncitizens to vote in federal elections, and there is no credible evidence that it has happened in significant numbers, or that there is an effort underway to illegally register undocumented immigrants to vote this election.
Voter registration forms require voters to sign an oath — under penalty of perjury — that they are U.S. citizens. If a noncitizen lies about their citizenship on a registration form and votes, they have created a paper trail of a crime that is punishable with jail time and deportation.
“The deterrent is incredibly strong,” David Becker, executive director of the Center for Election Innovation and Research, told NPR.
Election officials routinely verify information on voter registration forms, which ask registrants for either a driver’s license number or the last four digits of Social Security numbers.
In 2016, the Brennan Center for Justice surveyed local election officials in 42 jurisdictions with high immigrant populations and found 30 cases of suspected noncitizens voting out of 23.5 million votes cast, or 0.0001%.
Georgia Secretary of State Brad Raffensperger launched an audit in 2022 that found fewer than 1,700 suspected noncitizens had attempted to register to vote over the past 25 years. None were able to vote.
— NPR disinformation reporter Jude Joffe-Block
TRUMP: "[Harris] was the border czar. Remember that she was the border czar."
Republicans have taken to calling Harris the "border czar" as a way to blame her for increased migration to the U.S. and what they see as border security policy failures of the Biden administration.
There is no actual "border czar" position. In 2021, President Biden tasked Harris with addressing the root causes of migration from Central America.
The "root causes strategy ... identifies, prioritizes, and coordinates actions to improve security, governance, human rights, and economic conditions in the region," the White House said in a statement. "It integrates various U.S. government tools, including diplomacy, foreign assistance, public diplomacy, and sanctions."
While Harris has been scrutinized on the right, immigration advocates have also criticized Harris, including for comments in 2021 where she warned prospective migrants, "Do not come."
TRUMP: "You could do abortions in the seventh month, the eighth month, the ninth month, and probably after birth."
As ABC News anchor Linsey Davis mentioned during her real-time fact check, there is no state where it is legal to kill a baby after birth (Trump called it "execution"). A report from KFF earlier this year also noted that abortions “after birth” are illegal in every state.
According to the Pew Research Center, the overwhelming majority of abortions — 93% — take place during the first trimester. Pew says 1% take place after 21 weeks. Most of those take place before 24 weeks, the approximate timeline for fetal viability, according to a report by KFF Health News.
A separate analysis from KFF earlier this year noted that later abortions are expensive to obtain and offered by relatively few providers, and often occur because of medical complications or because patients face barriers earlier in their pregnancies.
“Nowhere in America is a woman carrying a pregnancy to term and asking for an abortion. That isn’t happening; it’s insulting to the women of America,” Harris said.
Harris also invoked religion in her response, arguing that “one does not have to abandon their faith” to agree that the government should not control reproductive health decisions.
As Davis also noted, Trump has offered mixed messages about abortion over the course of the campaign. He has bragged about his instrumental role in overturning Roe v. Wade , while appearing to backpedal on an issue that polling makes clear is a liability for Republicans.
— NPR political correspondent Sarah McCammon
TRUMP: The U.S. withdrawal from Afghanistan "was one of the most incompetently handled situations anybody has ever seen."
Trump and Republicans in Congress say President Biden is to blame for the fall of Kabul to the Taliban three years ago, and the chaotic rush at the airport where 13 U.S. troops died in a suicide bomb attack that killed nearly 200 Afghan civilians trying to flee. Of late, Republicans have been emphasizing Harris’ role . But the Afghanistan war spanned four U.S. presidencies , and it's important to note that it was the Trump administration that signed a peace deal that was basically a quick exit plan.
Trump regularly claims there were no casualties in Afghanistan for 18 months under his administration, and it’s not true, according to Pentagon records.
— NPR veterans correspondent Quil Lawrence
HARRIS: “There is not one member of the military who is in active duty in a combat zone in any war zone around the world for the first time this century.”
This is a common administration talking point, and it's technically true. But thousands of troops in Iraq and on the Syrian border are still in very dangerous terrain. U.S. troops died in Jordan in January on a base that keeps watch over the war with ISIS in Syria.
HARRIS: "I will not ban fracking. I have not banned fracking as vice president United States, and in fact, I was the tie-breaking vote on the inflation Reduction Act which opened new leases for fracking."
When she first ran for president in 2019, Harris had said she was firmly in favor of banning fracking — a stance she later abandoned when she joined President Biden’s campaign as his running mate.
In an interview with CNN last month, Harris attempted to explain why her position has changed from being against fracking to being in favor of it.
“What I have seen is that we can grow, and we can increase a clean energy economy without banning fracking,” Harris told CNN’s Dana Bash.
Under the Biden-Harris administration, the U.S. produced a record amount of oil last year — averaging 12.9 million barrels per day. That eclipsed the previous record of 12.3 million barrels per day, set under Trump in 2019. 2023 was also a record year for domestic production of natural gas . Much of the domestic boom in oil and gas production is the result of hydraulic fracturing or “fracking” techniques .
In addition to record oil and gas production, the Biden-Harris administration has also coincided with rapid growth of solar and wind power . Meanwhile, coal has declined as a source of electricity.
TRUMP: "I had a choice to make: Do I save [the Affordable Care Act] and make it as good as it can be, or do I let it rot? And I saved it."
During his presidency, Trump undermined the Affordable Care Act in many ways — for instance, by slashing funding for advertising and free "navigators" who help people sign up for a health insurance plan on HealthCare.gov. And rather than deciding to "save" the ACA, he tried hard to get Congress to repeal it, and failed. When pushed Tuesday on what health policy he would put in its place, he said he has "concepts of a plan."
Amid medicaid's 'unwinding,' many states work to expand health care access.
The Biden administration has reversed course from Trump's management of the Affordable Care Act. Increased subsidies have made premiums more affordable in the marketplaces, and enrollment has surged. The uninsurance rate has dropped to its lowest point ever during the Biden administration.
The Affordable Care Act was passed in 2010 and is entrenched in the health care system. Republicans successfully ran against Obamacare for about a decade, but it has faded as a campaign issue this year.
— NPR health policy correspondent Selena Simmons-Duffin
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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.
Swedish School of Library and Information Science, University of Borås, Sweden
Department of Arts and Cultural Sciences, Lund University, Sweden
Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden
The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated (Simon et al., 2023).
Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.
To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.
The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few. While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.
Evidence hacking and backfiring effects
Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.
The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.
However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.
Recommendations
Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.
Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.
Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.
Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.
Indexed journals* | 5 | 3 | 4 | 7 | 19 |
Non-indexed journals | 18 | 18 | 13 | 40 | 89 |
Student papers | 4 | 3 | 1 | 11 | 19 |
Working papers | 5 | 3 | 2 | 2 | 12 |
Total | 32 | 27 | 20 | 60 | 139 |
Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.
The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs. Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.
As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.
Environment | researchgate.net (13) | orcid.org (4) | easychair.org (3) | ijope.com* (3) | publikasiindonesia.id (3) |
Health | researchgate.net (15) | ieee.org (4) | twitter.com (3) | jptcp.com** (2) | frontiersin.org (2) |
A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster. Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”
The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).
Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.
Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.
We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .
We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.
The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.
To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.
We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”
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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).
The authors declare no competing interests.
The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).
This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.
All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X
The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.
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Vice President and Democratic presidential candidate Kamala Harris and former President and Republican presidential candidate Donald Trump speak during a presidential debate.
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Summary form only: Abstracts of articles that will be presented in future issues of this publication. Index Terms Scanning the Issue – Sept 2024 Additional Papers
Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research.
About this story. Additional development by Jake Crump. Editing by Sarah Frostenson, Kevin Uhrmacher and Donna Cassata. The debate reaction group was conducted during and after the Sept. 10 debate ...