For images where the creator is unknown, you can use the title or description in the author position.
[Photograph of a violent confrontation during the Hong Kong protests]. (2019). https://twitter.com/xyz11111112 | |
([Confrontation during Hong Kong protests], 2019) |
The AI-powered APA Citation Checker points out every error, tells you exactly what’s wrong, and explains how to fix it. Say goodbye to losing marks on your assignment!
Get started!
If you viewed an image in person rather than online—for example in a museum or gallery, or in another text—the source information is different.
For images viewed in a museum or gallery, you include the name and location of the institution where you viewed the image.
APA format | Last name, Initials. (Year). [Format]. Museum, Location. |
---|---|
Goya, F. (1819–1823). [Painting]. Museo del Prado, Madrid, Spain. | |
(Goya, 1819–1823) |
Location information includes the city, state/province (abbreviated), and country, e.g. Sydney, NSW, Australia. Omit the state/province if not applicable.
Citations for images sourced from a print publication such as a book , journal , or magazine include information about the print source in which the image originally appeared:
When you include the image itself in your paper, it should be properly formatted as an APA figure , with a number, a descriptive title, and an entry in your list of figures if you have one.
The title of a figure should appear immediately above the image itself, and will vary according to the type of image cited. For example, an artwork is simply the work’s title.
A note below the figure may include further details regarding its authorship and medium, copyright/permissions information, additional explanatory notes, or other elements.
Note that any figures that you didn’t create yourself should appear both in your list of figures (if you have one) and on your reference page . Figures you create yourself only appear in the list of figures.
In most styles, the title page is used purely to provide information and doesn’t include any images. Ask your supervisor if you are allowed to include an image on the title page before doing so. If you do decide to include one, make sure to check whether you need permission from the creator of the image.
Include a note directly beneath the image acknowledging where it comes from, beginning with the word “ Note .” (italicized and followed by a period). Include a citation and copyright attribution . Don’t title, number, or label the image as a figure , since it doesn’t appear in your main text.
If you adapt or reproduce a table or figure from another source, you should include that source in your APA reference list . You should also include copyright information in the note for the table or figure, and include an APA in-text citation when you refer to it.
Tables and figures you created yourself, based on your own data, are not included in the reference list.
APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures .
A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.
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 Citation Generator.
Caulfield, J. (2023, December 01). How to Cite an Image in APA Style | Format & Examples. Scribbr. Retrieved June 7, 2024, from https://www.scribbr.com/apa-examples/image/
Scribbr apa citation checker.
An innovative new tool that checks your APA citations with AI software. Say goodbye to inaccurate citations!
Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.
Q&A for work
Connect and share knowledge within a single location that is structured and easy to search.
So, I've seen a really nice figure in a paper; what's the best way to 'get a copy'?
Will it be on the publisher's website? Do I need to draw my own version? Email the author?
And, finally, how does the answer vary for (a) those wishing to republish the figure in their own work, (b) those not wishing to publish the figure e.g. for student coursework.
Unless the paper is available under a very permissive license, such as Creative Commons Attribution , you will need to seek permission. (There may be other legal possibilities, such as fair use or fair dealing, but that's a little subtle. See this story for more information on that.)
The copyright owner is the person you need permission from. Who that is will generally be marked on the published paper (often it is the publisher, and sometimes the author). If the publisher holds the copyright, then it is still polite to ask permission from the authors as well, although this is not legally required.
Big commercial publishers will often have a department for dealing with this, typically with a name like "Permissions". If you can't find such a department, then you can try just writing to the journal in question (look at their web page to try to find e-mail addresses).
If you are lucky, they will quickly approve your use of the figure. If you are not lucky, they will ask for money.
And, finally, how does the answer vary for (a) those wishing to republish the figure in their own work,
There are definitely legal issues here.
(b) those not wishing to publish the figure e.g. for student coursework.
If you never make the work available to the public, then it is hard to imagine that the copyright owner will ever learn about it or complain (and they would look foolish if they tried to sue someone for using their figure in a homework assignment). However, you still have a moral obligation to cite the source of the figure.
In addition to the answer by @AnonymousMathematician, it's important to remember how to cite a figure from another paper in your own . This link gives a good explanation on how to do so: for publications using the APA style guide, for instance, it should be in the format
Figure #. Description/Note. Adapted from “Title of Article,” by F. M. Author and C. D. Author, year, Title of Journal, volume, p. xx. Copyright year by the Name of Copyright Holder. Adapted [or Reprinted] with permission.
In case some one wants IEEE-related answer (similar to what other responders have said), see page 17 of this IEEE presentation :
Reuse of Published Materials You must cite and acknowledge any published materials that you make re-use of Examples: Diagrams/figures from an existing paper Extracted and re-used => must get permission from author/publisher (copyright owner) and cite and acknowledge Redrawn with modifications => should cite and indicated “adapted from” or “based on” This includes your own prior published work!
The prestigious American Institute of Physics (AIP) has a FAQ page that is golden:
https://publishing.aip.org/authors/author-permission-faq
Answered questions include:
Continuing with aeismail comment under ElCid's answer,
I edited this because different publishers have different guidelines. In some cases, you can say "Reprinted from Ref. XX with permission," and the longer copyright notice isn't required. – aeismail
The AIP states the following:
[...] The original publisher will provide you with their preferred wording for the credit line (in most cases). A credit line consisting only of “Used with permission” is not sufficient. An example of a typical complete credit line appears as: Reproduced with permission from J. Org. Chem. 63, 99 (1998). Copyright 1998 American Chemical Society. Note that even when reusing material in the public domain (for which obtaining permission does not apply), you must include an appropriate credit line, which states the original source. An example of an appropriate credit line for material in the public domain follows: Reprinted from A. H. Harvey and J. C. Bellows, Evaluation and Correlation of Steam Solubility Data for Salts and Minerals of Interest in the Power Industry, NIST Technical Note 1387 (U.S. GPO, Washington, DC, 1997). https://publishing.aip.org/authors/author-permission-faq
Not the answer you're looking for browse other questions tagged publications copyright graphics ., hot network questions.
The inclusion of visual elements such as images, diagrams, and graphs can be a powerful tool in effectively communicating the main points of a research paper. In this article, we discuss both the potential benefits and drawbacks to consider when deciding whether or not to include pictures within one’s research paper. Through an examination of the evidence that exists on this topic, along with highlighting perspectives from experts in various fields, we will provide readers with pertinent information for making decisions about using visuals as part of their written work.
Ii. the benefits of including pictures in a research paper, iii. factors to consider when inserting an image into a research paper, iv. negative aspects of images in research papers, v. strategies for finding appropriate and legitimate sources for pictures used within academic writing, vi. techniques for citing images correctly in mla formatting style, vii conclusion.
When embarking on any academic journey, it’s important to consider the importance of an introduction . Introductions not only offer a way for readers to grasp the main idea of a paper quickly and easily, but they also provide an opportunity for authors to draw in their audience through engaging writing.
Including images is one way that research papers can effectively engage readers. Adding visuals can help break up long passages or enhance explanations of key concepts throughout a text. For example, if discussing complicated processes related to biology or chemistry, diagrams may be included within the body of work as visual aids which explain more complex information.
A picture is worth a thousand words. This saying applies to research papers as well, which can be greatly enhanced by incorporating the right visuals. There are several benefits of including pictures in a research paper.
When incorporating an image into a research paper, there are several factors to consider. First , one must determine if the chosen image is relevant to the argument being made in the paper. For example, inserting a photograph of an historic building alongside evidence about its past inhabitants will add clarity and visual interest to this discussion. Moreover, readers can draw further connections between ideas or learn more about certain topics simply by viewing associated images.
Second , it is important that any visuals used within a research paper are properly cited according to style guidelines established by the academic institution or journal publishing outlet – plagiarism rules still apply! This ensures proper attribution for artistic work included in your writing and prevents readers from misinterpreting where source material has come from.
Lastly, always ensure that images complement rather than distract from text; while some visuals may be striking on their own, too many colorful diagrams or photographs could create confusion around main points being conveyed in written form. Remember: it’s not enough just to include an image—it should also provide clear value when paired with related content throughout your document.
One of the key considerations when producing a research paper is the use of images. While photographs and illustrations can add value to written content, it’s important that researchers understand both the positive and negative implications they may bring.
In many cases, photos used in research papers provide an overall idea or concept but don’t offer much detail. It’s possible that using additional figures would be beneficial for illustrating certain aspects not fully understood from just one image. In other words, if you’re aiming to explain a complicated concept, relying solely on pictures could cause readers to misinterpret your intentions – something which should be avoided at all costs!
When writing up research papers with any kind of visual aid such as diagrams or charts – there are two main options available: including them within the text body itself or linking out to external sources like websites hosting these resources online. The former requires more effort due to formatting requirements; while links take users away from reading material so must only be used where appropriate. Despite this though, providing visuals still has its place within academic literature; giving readers greater understanding into complex topics being discussed throughout by complementing theoretical ideas with tangible evidence presented via imaging tools.
Using Online Databases In the digital age, much of our research material is found on online databases. While these are incredibly useful for finding accurate and legitimate information from reliable sources, they can also be used to locate appropriate images. Many academic journals and other publications provide access to an array of photographs or diagrams which support their text. When searching through any online database, it’s important to ensure that whatever photos you use are permitted for educational purposes; often websites will contain a disclaimer specifying what usage rights have been granted.
Reputable Publishers & Digital Image Banks Another way of obtaining pictures related to your topic is by accessing reputable publishers’ collections or using digital image banks such as Getty Images and Unsplash which offer free downloads (under certain conditions). Using books specifically written about the subject matter may help too – some authors include illustrations in their writing so they could act as great visual aids when discussing a particular issue within your paper! Additionally, can a research paper have pictures? Yes – providing each photo is appropriately cited according to acceptable academic referencing standards alongside being labeled correctly with captions describing its context in relation to the given text.
Correctly Citing Images
Citations are a critical part of all academic writing, and it is essential to know how to cite an image correctly in MLA format. In the same way that you would include information from a book or journal article, citing images provides credit for visual sources so readers can track down the originals.
In this paper, we explored the potential of using pictures in research. To summarize:
When considering images for inclusion in one’s work, it is important to carefully consider their impact on the message being delivered. When included thoughtfully they can contribute significantly by conveying an idea with more clarity than text alone ever could. But when selected poorly or abused too frequently, they may detract from a reader’s overall understanding and experience of your work – ultimately damaging its effectiveness as persuasive evidence.
The inclusion of visual elements in a research paper can often times be beneficial to its content. Through the integration of pictures, graphs and diagrams, authors are able to more effectively communicate their ideas while also providing readers with additional insight into the topic at hand. However, it is important for researchers to understand both the pros and cons associated with incorporating visuals into their work so that they may make an informed decision regarding how best to utilize them within their own papers. In this way, not only does including images add value from a narrative perspective but also helps elevate overall scholarship standards by ensuring sound conclusions are drawn based on tangible evidence whenever possible.
Purdue Online Writing Lab Purdue OWL® College of Liberal Arts
This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.
Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.
The Online Writing Lab at Purdue University houses writing resources and instructional material, and we provide these as a free service of the Writing Lab at Purdue. Students, members of the community, and users worldwide will find information to assist with many writing projects. Teachers and trainers may use this material for in-class and out-of-class instruction.
The Purdue On-Campus Writing Lab and Purdue Online Writing Lab assist clients in their development as writers—no matter what their skill level—with on-campus consultations, online participation, and community engagement. The Purdue Writing Lab serves the Purdue, West Lafayette, campus and coordinates with local literacy initiatives. The Purdue OWL offers global support through online reference materials and services.
The Purdue OWL® is committed to supporting students, instructors, and writers by offering a wide range of resources that are developed and revised with them in mind. To do this, the OWL team is always exploring possibilties for a better design, allowing accessibility and user experience to guide our process. As the OWL undergoes some changes, we welcome your feedback and suggestions by email at any time.
Please don't hesitate to contact us via our contact page if you have any questions or comments.
All the best,
Facebook twitter.
More than 100 reference examples and their corresponding in-text citations are presented in the seventh edition Publication Manual . Examples of the most common works that writers cite are provided on this page; additional examples are available in the Publication Manual .
To find the reference example you need, first select a category (e.g., periodicals) and then choose the appropriate type of work (e.g., journal article ) and follow the relevant example.
When selecting a category, use the webpages and websites category only when a work does not fit better within another category. For example, a report from a government website would use the reports category, whereas a page on a government website that is not a report or other work would use the webpages and websites category.
Also note that print and electronic references are largely the same. For example, to cite both print books and ebooks, use the books and reference works category and then choose the appropriate type of work (i.e., book ) and follow the relevant example (e.g., whole authored book ).
Examples on these pages illustrate the details of reference formats. We make every attempt to show examples that are in keeping with APA Style’s guiding principles of inclusivity and bias-free language. These examples are presented out of context only to demonstrate formatting issues (e.g., which elements to italicize, where punctuation is needed, placement of parentheses). References, including these examples, are not inherently endorsements for the ideas or content of the works themselves. An author may cite a work to support a statement or an idea, to critique that work, or for many other reasons. For more examples, see our sample papers .
Reference examples are covered in the seventh edition APA Style manuals in the Publication Manual Chapter 10 and the Concise Guide Chapter 10
Textual works are covered in Sections 10.1–10.8 of the Publication Manual . The most common categories and examples are presented here. For the reviews of other works category, see Section 10.7.
Data sets are covered in Section 10.9 of the Publication Manual . For the software and tests categories, see Sections 10.10 and 10.11.
Audiovisual media are covered in Sections 10.12–10.14 of the Publication Manual . The most common examples are presented together here. In the manual, these examples and more are separated into categories for audiovisual, audio, and visual media.
Online media are covered in Sections 10.15 and 10.16 of the Publication Manual . Please note that blog posts are part of the periodicals category.
Suggestions or feedback?
Press contact :.
Previous image Next image
Researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Google Research may have just performed digital sorcery — in the form of a diffusion model that can change the material properties of objects in images. Dubbed Alchemist , the system allows users to alter four attributes of both real and AI-generated pictures: roughness, metallicity, albedo (an object’s initial base color), and transparency. As an image-to-image diffusion model, one can input any photo and then adjust each property within a continuous scale of -1 to 1 to create a new visual. These photo editing capabilities could potentially extend to improving the models in video games, expanding the capabilities of AI in visual effects, and enriching robotic training data.
The magic behind Alchemist starts with a denoising diffusion model: In practice, researchers used Stable Diffusion 1.5, which is a text-to-image model lauded for its photorealistic results and editing capabilities. Previous work built on the popular model to enable users to make higher-level changes, like swapping objects or altering the depth of images. In contrast, CSAIL and Google Research’s method applies this model to focus on low-level attributes, revising the finer details of an object’s material properties with a unique, slider-based interface that outperforms its counterparts. While prior diffusion systems could pull a proverbial rabbit out of a hat for an image, Alchemist could transform that same animal to look translucent. The system could also make a rubber duck appear metallic, remove the golden hue from a goldfish, and shine an old shoe. Programs like Photoshop have similar capabilities, but this model can change material properties in a more straightforward way. For instance, modifying the metallic look of a photo requires several steps in the widely used application.
“When you look at an image you’ve created, often the result is not exactly what you have in mind,” says Prafull Sharma, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead author on a new paper describing the work. “You want to control the picture while editing it, but the existing controls in image editors are not able to change the materials. With Alchemist, we capitalize on the photorealism of outputs from text-to-image models and tease out a slider control that allows us to modify a specific property after the initial picture is provided.”
Precise control
“Text-to-image generative models have empowered everyday users to generate images as effortlessly as writing a sentence. However, controlling these models can be challenging,” says Carnegie Mellon University Assistant Professor Jun-Yan Zhu, who was not involved in the paper. “While generating a vase is simple, synthesizing a vase with specific material properties such as transparency and roughness requires users to spend hours trying different text prompts and random seeds. This can be frustrating, especially for professional users who require precision in their work. Alchemist presents a practical solution to this challenge by enabling precise control over the materials of an input image while harnessing the data-driven priors of large-scale diffusion models, inspiring future works to seamlessly incorporate generative models into the existing interfaces of commonly used content creation software.”
Alchemist’s design capabilities could help tweak the appearance of different models in video games. Applying such a diffusion model in this domain could help creators speed up their design process, refining textures to fit the gameplay of a level. Moreover, Sharma and his team’s project could assist with altering graphic design elements, videos, and movie effects to enhance photorealism and achieve the desired material appearance with precision.
The method could also refine robotic training data for tasks like manipulation. By introducing the machines to more textures, they can better understand the diverse items they’ll grasp in the real world. Alchemist can even potentially help with image classification, analyzing where a neural network fails to recognize the material changes of an image.
Sharma and his team’s work exceeded similar models at faithfully editing only the requested object of interest. For example, when a user prompted different models to tweak a dolphin to max transparency, only Alchemist achieved this feat while leaving the ocean backdrop unedited. When the researchers trained comparable diffusion model InstructPix2Pix on the same data as their method for comparison, they found that Alchemist achieved superior accuracy scores. Likewise, a user study revealed that the MIT model was preferred and seen as more photorealistic than its counterpart.
Keeping it real with synthetic data
According to the researchers, collecting real data was impractical. Instead, they trained their model on a synthetic dataset, randomly editing the material attributes of 1,200 materials applied to 100 publicly available, unique 3D objects in Blender, a popular computer graphics design tool. “The control of generative AI image synthesis has so far been constrained by what text can describe,” says Frédo Durand, the Amar Bose Professor of Computing in the MIT Department of Electrical Engineering and Computer Science (EECS) and CSAIL member, who is a senior author on the paper. “This work opens new and finer-grain control for visual attributes inherited from decades of computer-graphics research.” "Alchemist is the kind of technique that's needed to make machine learning and diffusion models practical and useful to the CGI community and graphic designers,” adds Google Research senior software engineer and co-author Mark Matthews. “Without it, you're stuck with this kind of uncontrollable stochasticity. It's maybe fun for a while, but at some point, you need to get real work done and have it obey a creative vision."
Sharma’s latest project comes a year after he led research on Materialistic , a machine-learning method that can identify similar materials in an image. This previous work demonstrated how AI models can refine their material understanding skills, and like Alchemist, was fine-tuned on a synthetic dataset of 3D models from Blender.
Still, Alchemist has a few limitations at the moment. The model struggles to correctly infer illumination, so it occasionally fails to follow a user’s input. Sharma notes that this method sometimes generates physically implausible transparencies, too. Picture a hand partially inside a cereal box, for example — at Alchemist’s maximum setting for this attribute, you’d see a clear container without the fingers reaching in. The researchers would like to expand on how such a model could improve 3D assets for graphics at scene level. Also, Alchemist could help infer material properties from images. According to Sharma, this type of work could unlock links between objects' visual and mechanical traits in the future.
MIT EECS professor and CSAIL member William T. Freeman is also a senior author, joining Varun Jampani, and Google Research scientists Yuanzhen Li PhD ’09, Xuhui Jia, and Dmitry Lagun. The work was supported, in part, by a National Science Foundation grant and gifts from Google and Amazon. The group’s work will be highlighted at CVPR in June.
Related links.
Previous item Next item
Read full story →
Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA
Clip-adapter: better vision-language models with feature adapters, learning to prompt for vision-language models, eurosat: a novel dataset and deep learning benchmark for land use and land cover classification, related papers.
Showing 1 through 3 of 0 Related Papers
Numbers, Facts and Trends Shaping Your World
Read our research on:
Full Topic List
Read Our Research On:
Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.
Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.
To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.
Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.
Here are the questions used for this analysis , along with responses, and its methodology .
A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms Twitter and X are both used in this report to refer to the same platform.
YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.
Year | YouTube | TikTok | Snapchat | Twitter (X) | BeReal | Nextdoor | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
8/5/2012 | 54% | 9% | 10% | 16% | 13% | |||||||
8/7/2012 | 14% | |||||||||||
12/9/2012 | 11% | 13% | 13% | |||||||||
12/16/2012 | 57% | |||||||||||
5/19/2013 | 15% | |||||||||||
7/14/2013 | 16% | |||||||||||
9/16/2013 | 57% | 14% | 17% | 17% | 14% | |||||||
9/30/2013 | 16% | |||||||||||
1/26/2014 | 16% | |||||||||||
9/21/2014 | 58% | 21% | 22% | 23% | 19% | |||||||
4/12/2015 | 62% | 24% | 26% | 22% | 20% | |||||||
4/4/2016 | 68% | 28% | 26% | 25% | 21% | |||||||
1/10/2018 | 73% | 68% | 35% | 29% | 25% | 22% | 27% | 24% | ||||
2/7/2019 | 73% | 69% | 37% | 28% | 27% | 20% | 24% | 22% | 11% | |||
2/8/2021 | 81% | 69% | 40% | 31% | 21% | 28% | 23% | 25% | 23% | 18% | 13% | |
9/5/2023 | 83% | 68% | 47% | 35% | 33% | 30% | 29% | 27% | 22% | 22% | 3% |
Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.
Source: Surveys of U.S. adults conducted 2012-2023.
Usage of the major online platforms varies by factors such as age, gender and level of formal education.
% of U.S. adults who say they ever use __ by …
Ages 18-29 | 30-49 | 50-64 | 65+ | |
---|---|---|---|---|
67 | 75 | 69 | 58 | |
78 | 59 | 35 | 15 | |
32 | 40 | 31 | 12 | |
Twitter (X) | 42 | 27 | 17 | 6 |
45 | 40 | 33 | 21 | |
Snapchat | 65 | 30 | 13 | 4 |
YouTube | 93 | 92 | 83 | 60 |
32 | 38 | 29 | 16 | |
44 | 31 | 11 | 3 | |
TikTok | 62 | 39 | 24 | 10 |
BeReal | 12 | 3 | 1 | <1 |
Men | Women | |
---|---|---|
59 | 76 | |
39 | 54 | |
31 | 29 | |
Twitter (X) | 26 | 19 |
19 | 50 | |
Snapchat | 21 | 32 |
YouTube | 82 | 83 |
27 | 31 | |
27 | 17 | |
TikTok | 25 | 40 |
BeReal | 2 | 5 |
White | Black | Hispanic | Asian* | |
---|---|---|---|---|
69 | 64 | 66 | 67 | |
43 | 46 | 58 | 57 | |
30 | 29 | 23 | 45 | |
Twitter (X) | 20 | 23 | 25 | 37 |
36 | 28 | 32 | 30 | |
Snapchat | 25 | 25 | 35 | 25 |
YouTube | 81 | 82 | 86 | 93 |
20 | 31 | 54 | 51 | |
21 | 14 | 23 | 36 | |
TikTok | 28 | 39 | 49 | 29 |
BeReal | 3 | 1 | 4 | 9 |
Less than $30,000 | $30,000- $69,999 | $70,000- $99,999 | $100,000+ | |
---|---|---|---|---|
63 | 70 | 74 | 68 | |
37 | 46 | 49 | 54 | |
13 | 19 | 34 | 53 | |
Twitter (X) | 18 | 21 | 20 | 29 |
27 | 34 | 35 | 41 | |
Snapchat | 27 | 30 | 26 | 25 |
YouTube | 73 | 83 | 86 | 89 |
26 | 26 | 33 | 34 | |
12 | 23 | 22 | 30 | |
TikTok | 36 | 37 | 34 | 27 |
BeReal | 3 | 3 | 3 | 5 |
High school or less | Some college | College graduate+ | |
---|---|---|---|
63 | 71 | 70 | |
37 | 50 | 55 | |
10 | 28 | 53 | |
Twitter (X) | 15 | 24 | 29 |
26 | 42 | 38 | |
Snapchat | 26 | 32 | 23 |
YouTube | 74 | 85 | 89 |
25 | 23 | 39 | |
14 | 23 | 30 | |
TikTok | 35 | 38 | 26 |
BeReal | 3 | 4 | 4 |
Urban | Suburban | Rural | |
---|---|---|---|
66 | 68 | 70 | |
53 | 49 | 38 | |
31 | 36 | 18 | |
Twitter (X) | 25 | 26 | 13 |
31 | 36 | 36 | |
Snapchat | 29 | 26 | 27 |
YouTube | 85 | 85 | 77 |
38 | 30 | 20 | |
29 | 24 | 14 | |
TikTok | 36 | 31 | 33 |
BeReal | 4 | 4 | 2 |
Rep/Lean Rep | Dem/Lean Dem | |
---|---|---|
70 | 67 | |
43 | 53 | |
29 | 34 | |
Twitter (X) | 20 | 26 |
35 | 35 | |
Snapchat | 27 | 28 |
YouTube | 82 | 84 |
25 | 33 | |
20 | 25 | |
TikTok | 30 | 36 |
BeReal | 4 | 4 |
This fact sheet was compiled by Research Assistant Olivia Sidoti , with help from Research Analyst Risa Gelles-Watnick , Research Analyst Michelle Faverio , Digital Producer Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher Eugenie Park .
Follow these links for more in-depth analysis of the impact of social media on American life.
Find more reports and blog posts related to internet and technology .
1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 | Media Inquiries
ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .
© 2024 Pew Research Center
If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.
Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).
Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.
In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.
Related articles.
IMAGES
VIDEO
COMMENTS
The image below was found through Google Images and downloaded from the internet. It can be used in a critical context within a presentation, classroom session, or paper/thesis, as follows: [Figure 2. This image shows the interior of Bibliotheca Alexandrina designed by the Norwegian architecture firm Snøhetta in 2001. Image downloaded from ...
Include as much of the information below when citing images in a paper and formal presentations. Apply the appropriate citation style (see below for APA, MLA examples). Image creator's name (artist, photographer, etc.) Title of the image; Date the image (or work represented by the image) was created; Date the image was posted online
Many scholarly publications are enhanced with images, ranging from reproductions of fine art to graphs showing the results of scientific research. Including images in books and articles can complement the text, visually demonstrate the author's analysis, and engage the reader. Using images in publications, however, raises copyright issues ...
1. Editable Images. The best kind of science images are editable vector files that allow you to customize the designs to best match the main points of your research. These include image file types such as Scalable Vector Graphics (.svg), Adobe Illustrator (.ai), Affinity Designer (.afdesign), Encapsulated PostScript (.eps), and some files in ...
In research papers, pictures can be used as an effective tool to capture the reader's attention and illustrate a point. In fact, studies have shown that images make more of an impact than words alone. Pictures in research papers come in all shapes and sizes; from graphs to diagrams or photographs - these visuals help enhance arguments while ...
The only official, authorized book on MLA style. The new, ninth edition builds on the MLA's unique approach to documenting sources using a template of core elements facts, common to most sources, like author, title, and publication date that allows writers to cite any type of work, from books, e-books, and journal articles in databases to song lyrics, online images, social media posts ...
Including pictures in research papers has become an increasingly important part of academic writing. As the use of visuals to convey ideas and messages becomes more commonplace, there is a need for academics to understand how best to incorporate images into their work. This article provides guidance on when and how illustrations should be used ...
It is important to analyze and evaluate images you use for research, study, and presentations. Images should be analyzed and evaluated like any other source, such as journal articles or books, to determine their quality, reliability, and appropriateness. Images should be analyzed evaluated on several levels. Visual analysis is an important step ...
II. Benefits of Using Pictures in Research Papers. Pictures can be an effective way of engaging readers and bringing research papers to life. Not only do images help readers better understand the concepts being presented, they also provide visual cues that allow complex ideas to be digested quickly.
But that's what we're doing when we use images from a Google Image search or other simple search without being careful! Luckily there are many ways to find great images without violating the rights of artists, photographers, or organizations. A reminder that your Instagram photos aren't really yours: Someone else can sell them for $90,000 ...
The use of images in research papers can bring many benefits, making them valuable tools for researchers and authors. Some of the key benefits include: Enhancing readability and engagement: Images can make research papers more visually appealing and engaging, encouraging readers to stay focused and interested in the work. They can also help to ...
For simplicity, the examples in this article will focus entirely on how to cite digital (internet) pictures. MLA style. Format: Image Creator's Last Name, First Name. "Image Title.". Website Name, Day Month Year Published, URL. Example: Jones, Daniel. "The Hope Creek nuclear plant.".
For formal papers and presentations provide BOTH a caption and a citation in your bibliography or works cited. For example, this image was found using a Google Image Search. The image is hyperlinked back to the original source (on Flickr) and as much information as is known about the image is included in the caption below.
While UNM's Digital Respository does not have an institutional policy on the use of images in theses and dissertations, ... write it on paper, save on hard drive or other storage device, take the photograph, record the music, etc.) your thesis or dissertation is copyrighted. ... Center for Southwest Research/Special Collections, Room 127B. 505 ...
Using and citing images in a research paper as already explained can make your research paper more understanding and structured in appearance. For this, you can use photos, drawings, charts, graphs, infographics, etc. However, there are no mandatory regulations to use or cite images in a research paper, but there are some recommendations as per ...
You can find images on the web but you should be concerned with using images legally and ethically. Use the resources to the right to locate free and reusable images. You can legally use photos in four ways: 1) find photos that are licensed as Creative Commons (flickr) , 2) ask permission from the photographer, 3) buy your photos from a stock ...
Finally, while a lot of data is helpful to have, be sure to reduce the presence of "chartjunk" - the unnecessary visual elements that distract the reader from what really matters…your data! There are various tools/platforms to help you create high-quality images for research papers including R, ImageJ, ImageMagick, Cytospace, and more.
Citing images accessed online. For online images, include the name of the site you found it on, and a URL. Link directly to the image where possible, as it may be hard to locate from the other information given. APA format. Last name, Initials. ( Year ). Image title [ Format ]. Site Name.
Unless the paper is available under a very permissive license, such as Creative Commons Attribution, you will need to seek permission. (There may be other legal possibilities, such as fair use or fair dealing, but that's a little subtle. See this story for more information on that.) The copyright owner is the person you need permission from.
A third category is Fair Use, which is a determination of eligibility via four factors (the purpose/character of the use; the nature of the copyrighted work; the amount of the work used; the effect of use on the work's value or market). Using an image in a dissertation or thesis under this provision requires significant research. A very good
Visual methodologies are used to understand and interpret images (Barbour, 2014) and include photography, film, video, painting, drawing, collage, sculpture, artwork, graffiti, advertising, and cartoons.Visual methodologies are a new and novel approach to qualitative research derived from traditional ethnography methods used in anthropology and sociology.
Introduction. Educational research related to visual literacy and its connections to children's reading and children's literature has flourished since the publication of Arizpe and Styles' seminal text, Children Reading Pictures (Citation 2003), now in its third edition co-authored with Kate Noble (Citation 2023).In the first edition, the authors noted that their review of the research ...
IV. Negative Aspects of Images in Research Papers. One of the key considerations when producing a research paper is the use of images. While photographs and illustrations can add value to written content, it's important that researchers understand both the positive and negative implications they may bring. Lack of Detail
Mission. The Purdue On-Campus Writing Lab and Purdue Online Writing Lab assist clients in their development as writers—no matter what their skill level—with on-campus consultations, online participation, and community engagement. The Purdue Writing Lab serves the Purdue, West Lafayette, campus and coordinates with local literacy initiatives.
In this era of Research and Technology, Image processing has become one of the most critical fields. Using a number of researchers' papers, we are going to study appropriate methods or algorithm, their accuracy, a key feature of each paper. All research papers going to study in the context of Android technology.
More than 100 reference examples and their corresponding in-text citations are presented in the seventh edition Publication Manual.Examples of the most common works that writers cite are provided on this page; additional examples are available in the Publication Manual.. To find the reference example you need, first select a category (e.g., periodicals) and then choose the appropriate type of ...
Using a synthetic dataset, a new image-to-image diffusion model known as Alchemist can adjust ... and Google Research may have just performed digital sorcery — in the form of a diffusion model that can change the material properties of objects in images. ... and lead author on a new paper describing the work. "You want to control the ...
A novel CLIP adaptation method, named Proto-Adapter, which employs a single-layer adapter of constant size regardless of the amount of training data and even outperforms Tip-Adapter, which enables training-free adaptation. Large vision-language models, such as Contrastive Vision-Language Pre-training (CLIP), pre-trained on large-scale image-text datasets, have demonstrated robust zero-shot ...
Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape. To better understand Americans' social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023.
If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using—and deriving business value from—this new technology.In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago.