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A new bibliometric map, practical steps for libraries, and research gaps for MLIS students
What can librarians actually do about misinformation? Patrons bring false claims to reference desks and expect a straight answer, but the profession still has few shared playbooks for sorting truth from harm. A mapping study published in the September 2026 Journal of Information Science (online September 21, 2026) reviewed Scopus-indexed LIS research on information disorder and found library responses clustered around literacy, professional roles, and institutional policy, while theory building remained uneven.
That gap is the practical tension facing students choosing the right MLIS program and librarians today: the field has developed responses, but not yet a shared framework for evaluating which ones change patron behavior.
Information disorder is the umbrella term for polluted information, and it splits into three types based on two questions: is the content false, and is someone sharing it to cause harm? When a library lumps all three together as "fake news," it tends to design one generic workshop that fits none of them and to collect data that can't show which problem it actually addressed. The same blurring weakens research, because studies that define terms differently are hard to compare or build on. That lack of shared vocabulary is one reason theoretical development in the field remains uneven.
| Term | Core definition | Is it false? | Intent to harm? | Library-desk example |
|---|---|---|---|---|
| Information disorder | Umbrella term for the full range of false, misleading, or harmfully used information circulating in a community or online environment | Varies (can be false or genuine) | Varies (depends on the type) | A librarian planning a semester of programming that covers health rumors, election hoaxes, and privacy leaks under one theme |
| Misinformation | False or inaccurate information shared by someone who believes it is true and does not intend harm | Yes | No | A patron asks for books confirming a mistaken health claim, such as a supplement curing a serious illness, after seeing it shared by a relative |
| Disinformation | False information deliberately created or spread to deceive, manipulate, or cause harm | Yes | Yes | A coordinated hoax circulates a fabricated flyer claiming the library has changed polling-site hours, and patrons call the desk to confirm |
| Malinformation | Genuine information shared out of context or exposed specifically to cause harm | No (the content is real) | Yes | A patron's private borrowing history or personal details are leaked and posted online to harass or embarrass them |
On September 21, 2026, University of Santo Tomas announced the online publication of "Mapping information disorder research in library and information science: A scoping review and bibliometric analysis" in the Journal of Information Science (DOI 10.1177/01655515261480638). Led by Sr. Lib. Maria Cecilia D. Lobo, RL, MLIS, with co-authors from De La Salle University, the study is one of the few attempts to step back and map how our own field has engaged with misinformation, disinformation, and related harms.
The researchers combined two techniques you can replicate for any emerging topic.
Available announcements do not confirm the exact document count, the precise data-collection cutoff, or the number of coupling clusters, so treat any specific figure with caution. What the sources do establish is the shape of the field, not a headcount.
The headline finding is a useful mirror. LIS has responded to information disorder mainly through three channels, and all three are strong:
What lags is theory. The authors describe theoretical development as uneven, meaning the field has plenty of applied activity but fewer original models that explain how information disorder works in library contexts or how interventions actually change behavior.
For a working librarian, that gap is reassuring in one sense: the instructional and policy tools you rely on rest on a coherent body of practice. But it also signals that when you reach for a theory to justify a program, you may find the scholarship thinner than the practice. The authors frame their map as a foundation for future work, and that framing is an open invitation to the reader's own research agenda for the future of librarianship.
Library and information science has answered information disorder with literacy, professional, and institutional responses, but theoretical development in the field remains uneven.
Every library type meets misinformation, but audiences, risks, and survey evidence differ sharply, and special libraries remain almost undocumented in recent research. Across all four types, the shared responsibilities are teaching people how to evaluate sources and giving staff the training and written guidance that surveys repeatedly show is missing. The main differences are how much time each setting allows for instruction and who is accountable for delivering it.
| Library type | Typical audience | Main misinformation risks | Realistic responsibilities | Documented practices and survey findings |
|---|---|---|---|---|
| Public libraries | General community members of all ages, including patrons with limited digital skills | Health, civic, and local news claims encountered through social media and everyday searching | Reference help, community programs, and staff able to model source evaluation at the desk | A 2026 survey of 372 public library staff in England found most libraries did not provide dedicated guidance or educational resources on misinformation. In the international Digital Resilience to Disinformation study (Cogitatio Press), 65.4% of participating librarians (51 of 78) had received no media and information literacy training before the project. |
| Academic libraries | Undergraduates, graduate students, and faculty | Unreliable sources in coursework and research, and weak evaluation of scholarly and popular content | Integrating misinformation into information literacy instruction alongside teaching faculty | In the ACRL follow-up study published in College & Research Libraries (2023), 95.7% of academic librarians were concerned about misinformation (87.8% strongly), 92% said higher education instructors are responsible for teaching these skills, and 78.5% already address misinformation in instruction. A 2026 Taylor & Francis study of Australian academic librarians found 79% strongly concerned and 98% agreeing information literacy competencies matter in combating mis- and disinformation. |
| School libraries | K-12 students and teachers | Viral content and social media claims reaching students before they have evaluation habits | Teaching age-appropriate information literacy, often squeezed by limited class time | A 2025 study by ALISE and the University of Illinois at Urbana-Champaign found 49.4% of school librarians had zero minutes available to teach information literacy. Only 14.2% had a comprehensive range of resources, 70.2% had some, and 15.6% had none identified. |
| Special libraries | Employees, researchers, clinicians, or legal and corporate professionals within one organization | Flawed sources informing organizational, clinical, or legal decisions | Vetting sources and content for specialized users | Recent survey data specific to special librarians and misinformation is thin, so concern levels, training, and practices are not well documented. |
Instruction has become the information literacy librarian's default answer to information disorder, yet the evidence for specific classroom tactics still trails the enthusiasm. The mapping study above found that literacy-oriented responses dominate the LIS literature. The practical question is which habits are worth teaching and how honestly to describe their results.
Project a screenshot of a viral post (health or local news works well). Minutes one and two: ask what learners notice and whether they would share it. Minutes three to seven: pairs read laterally, recording who posted it, what two outside sources say, and where the claim originated. Minutes eight to ten: debrief on which move produced an answer fastest, then name that move as a habit to reuse.
The strongest quantitative library results concern reading, not misinformation. A 2026 meta-analysis of university library reading promotion pooled 15 studies and found a moderate effect (d = 0.52, 95% confidence interval 0.41 to 0.63). Heterogeneity was high (I² of 79.7%), study sizes ranged from 27 participants to thousands, and the paper reports both 22,321 total participants and 3,847 in its analytic synthesis.1 A Chicago randomized trial with 300 low-income families found that digital library access alone raised children's literacy by 0.29 standard deviations; added behavioral messages brought no extra benefit. Project Pustakalaya in Delhi (645 students across 30 intervention and 10 comparison schools) reported significant literacy gains, though available summaries give no effect size.2
For lateral reading, civic online reasoning curricula, and prebunking, we could not verify effect sizes from the sources reviewed, and the broader literature leans toward small-to-moderate, short-term outcomes with limited samples. A Mathematica review for IMLS, which narrowed 336 manuscripts to 84 included studies, likewise described evidence for most library activities as thin and context-dependent.3
The takeaway: debunking a single claim fades quickly, while a repeatable habit like lateral reading travels to the next post. Teach the move, then check weeks later whether learners still use it.
A 2026 evaluation found GPTZero flagged 8.9% of human text as AI-generated and Pangram flagged 15.0%, while one open-source detector flagged 30% to 69% of human writing as machine-generated.2 Those numbers matter because no single tool establishes truth, and ALA guidance treats AI outputs as drafts needing human review.
For any AI-generated summary or citation:
1. Verify that every citation exists in the library catalog, database, or publisher site. 2. Trace each claim by finding primary sources, such as official statistics, court records, legislation, filings, or repository data. 3. Cross-check with a second tool or trusted source, then compare publication dates, reporting periods, geography, and methodology.
Current detectors show unstable results. Beyond the 8.9% and 15.0% flag rates above, one analysis found over 61% of non-native English essays were falsely flagged, with Chinese students at 61.3% versus 5.1% for US students.2 Performance shifts by model, length, paraphrasing, language, and threshold, so treat any score as a lead, not proof. Do not use a score alone to reject, accuse, or discipline. If triaging, save the detector name, version, date, threshold, and output, then look for corroboration such as drafts, version history, source notes, or oral explanation. Reverse image search and claim databases also have limits: a match shows circulation, not authenticity, and absence from a claim database is not evidence of truth.
Add one question to every vendor negotiation: "Will you document and disclose how your search and recommendation algorithms rank, personalize, or omit content?" If the vendor will not answer, treat that opacity as part of the evaluation.
Can a library push back on false information without becoming a censor? Yes, as long as the tools are instruction and context rather than removal. ALA's materials support that approach, though the ones reviewed here do not include a standalone policy titled misinformation, disinformation, or fake news. The guidance sits inside the Library Bill of Rights and its interpretations.
The Library Bill of Rights (adopted June 19, 1939; amended through June 28, 2025) calls for materials that represent all points of view. It opposes removal because of partisan or doctrinal disapproval and commits libraries to challenging censorship and protecting confidentiality. The Interpretations of The Library Bill of Rights add that providing a work is not endorsing it, and that labeling and rating systems can restrict access. The Privacy: An Interpretation of the Library Bill of Rights, the ALA advocacy privacy pages1, and the Equity, Diversity, Inclusion, and Belonging interpretation2 all call for safeguarding library-use data. The Intellectual Freedom Principles for Academic Libraries specifically put instruction alongside collections and services. IFLA has its own statements on fake news and misinformation. Read those directly before quoting them, because the ALA materials here do not cover them.
For elections and health emergencies, a simple sequence keeps responses consistent. These steps are practical suggestions, not ALA requirements.
Evaluating a program does not require watching patrons. Log question topics in aggregate without names, keep attendance as counts, and avoid retaining borrowing or personal-use records longer than operations need. Anonymous exit surveys tell you more than identifiable histories and put no one's reading at risk.
"The library provides access to a broad range of viewpoints and does not endorse the ideas in the materials it holds. We do not remove or label items because we judge their content to be inaccurate. Instead, we teach evaluation skills, offer guides that place information in context, and point patrons to authoritative sources, especially during elections and public emergencies. Patron questions and program data are collected only in aggregate, retained no longer than necessary, and never linked to an individual's reading or research."
Start with outputs like attendance, then move toward outcomes: self-reported learning, short-term behavior change, and measured skill gains. None of the established frameworks below is a standardized misinformation test, so ACRL's Standards for Libraries in Higher Education advise choosing indicators and methods that fit your own mission. Pair at least one self-report measure with one direct measure of skill.
| Metric | What it tells you | How to collect it | Limitation |
|---|---|---|---|
| Attendance and reach (output) | How many people your sessions, workshops, or guides reached. | Headcounts, registrations, or session tallies. Privacy caution: record totals, not names. | Shows exposure only. It says nothing about learning or changed habits. |
| Immediate patron-reported outcomes (Project Outcome immediate survey) | Reported gains in knowledge, confidence, application, and awareness, plus intention to change behavior. | A six-question survey with Likert-scale and open-ended items, given right after the program. Privacy caution: keep responses anonymous and avoid collecting identifiers. | Self-reports do not prove fact-checking skill or show that the program caused the change. |
| Short-term behavior change (Project Outcome follow-up survey) | Whether patrons say they changed their behavior or kept benefiting from the program. | An optional five-question survey four to eight weeks later. Privacy caution: follow-up needs contact details, so ask for consent and delete contacts once the survey closes. | Captures reported behavior over a short window. It is not evidence of durable, long-term effects. |
| Pre/post skill gains (local test built on ACRL information literacy outcomes) | Whether learners improved on skills your program targets, such as evaluating a source. | Write a local pre- and post-test using ACRL's outcome guidelines. Privacy caution: match pre and post results with anonymous codes, not student names. | ACRL outcomes are guidelines, not a mandated instrument. Results are not comparable across institutions unless the local measures are harmonized. |
| Longitudinal program growth (ACRL program assessment) | Whether an instruction program produces more information-literate students over time. | Give a general library-knowledge test to incoming first-year students, repeat it at a midpoint, and again near graduation. Privacy caution: de-identify data that links students across years. | The test measures general library knowledge. It is not misinformation-specific and has no universal scoring threshold. |
| Qualitative evidence (diaries, focus groups) | How learners experience and apply source evaluation in practice. | Student information-literacy diaries and focus groups. Privacy caution: these can reveal personal or political beliefs, so get consent and anonymize quotes. | Tracks experiences and change over time, but there is no standardized scoring method. |
A workshop that fills every seat but changes no one's evaluation habits has measured attendance, not impact. Count what participants can do afterward, not how many showed up.
A descriptive map of the field is one thing; a testable explanation of why a library intervention works is another. The September 2026 Journal of Information Science mapping study makes that contrast explicit. Its finding that LIS has produced literacy-oriented, professional, and institutional responses to information disorder is useful, but the uneven theoretical development means students can still claim fresh territory. Because the study's map shows a practice-heavy footprint, theory-building and effectiveness evidence remain wide open for student work.
A strong capstone or thesis chooses one library setting, one population, and one measurable outcome. For example, a student might test how a 30-minute lateral reading exercise affects community college students' detection of health misinformation. That scale is manageable within a semester or two. If you want to build theory, start with a single proposition, such as "public libraries reduce misinformation harm only when staff are trained in trauma-informed reference." Then test it against a small set of observations. A weak scope tries to review all LIS misinformation scholarship, measure long-term behavior change across an entire university, or rely only on self-reported confidence. Those projects collapse under their own ambition.
Do not equate attendance with impact. Do not assume that presenting "both sides" of a false claim is neutral. Do not use internal data field names or dataset identifiers in your write-up. Instead, frame the project in human terms and report what participants could actually do differently after an intervention. For instance, say that participants identified 20 percent more manipulated headlines, not just that they felt more confident.
Before you can theorize about information disorder, you first need a map of how library and information science has already responded. Mapping is the necessary starting point for future scholarship.
The five steps below follow the approach behind the 2026 information disorder mapping study, and you can reuse them for any emerging LIS topic. Free tools commonly used for this kind of work include VOSviewer, CiteSpace, and the Bibliometrix R package with its Biblioshiny interface. One caution: clean and merge your keywords before mapping, because synonyms, plurals, and spelling variants can split one topic into several false clusters.
