[{"content":"When I was in 6th grade, we often took \u0026rsquo;trips\u0026rsquo; to the computer lab during science class. Towards the beginning of the year, we were conducting an ecological report on an animal of our choosing, and our teacher Mrs. Blake taught us a lesson about finding credible sources and citing them. Here were the basic tenants I learned:\n✔️ Whenever possible, reference .edu and .gov sources\n❌ Do not cite Wikipedia as your only source\n✅ Look at Wikipedia\u0026rsquo;s bibliography and click through to find the original sources\nThis was the first introduction to evaluating credibility that I can remember. It was rudimentary, but it was a start.\nAs I continued along my academic career, my evaluating credibility became more nuanced. For example, I realized that, although Mrs. Blake\u0026rsquo;s instructions not to cite Wikipedia were sensible, it didn\u0026rsquo;t mean Wikipedia was never credible or correct.\nIn fact, many times Wikipedia is credible and correct. Wikipedia articles are often written, developed, revised, and iterated-upon by nerds (like me) who are extremely passionate about niche topics—in addition to the fact that the citations for claims are often referenced in-text and with a bibliography below.\nAs I moved through high school and university, I learned that Wikipedia is not useless. It is a useful tool for finding information quickly, but it should not be the end-all and be-all of research.\nA lot has changed about how information is gathered since I was in sixth grade. We had Google back then, but Google was not the same tool it is today. Natural language processing (NLP)—the technology behind search, language translation, voice assistants, and chatbots like ChatGPT and Gemini—was nowhere near its current capabilities.\nWhat\u0026rsquo;s more? It\u0026rsquo;s rapidly accelerating. Rapidly.\nLarge Language Models (LLMs), an NLP technology, are beginning to appear everywhere. Sure, we interact with them when we are explicitly using a tool like ChatGPT, Gemini, or Copilot. However, they are also making their way into every aspect of our technological lives; we can find them on eCommerce websites (e.g. Rufus for Amazon), social media platforms (e.g. Grok for X), and even our Google Searches and email inboxes (e.g. Gemini for Search and Gmail). These tools are extremely helpful, and they can support efficiency in a world driven by extraordinary amounts of information.\nHowever, these AI tools are starting to do much of our reading and researching for us. For example, a survey analysis by McKinsey found that people report that about 50% of their Google Searches begin with AI Summaries (like the one below), and this figure is projected to raise to 75% by 2028.\nNot only do these summaries pop up, but research from the Pew Research Center (2025) found that while about 15% of people click through on a regular Google Search, only about 8% of people click through when they see an AI Summary. That suggests these AI summaries are reducing Google Searchers\u0026rsquo; direct interactions with sources by half.\nThis raises several questions: What happens when we stop reading sources directly? Can we trust these summaries, overviews, and reviews? How do we know whether the information we receive is correct?\nWhy LLM training matters The way LLMs are built and trained is an important part of answering these questions.\nFirst, they are pre-trained on large-scale text datasets (for example, books, articles, and websites). In this phase, the model learns to predict the next word in a sentence, and from that it picks up grammar, style, and a broad base of world knowledge. Next, they are fine-tuned on more carefully curated, smaller datasets—often including example conversations and human feedback—to make them better at following instructions and performing specific kinds of tasks.\nWhere are these datasets coming from, though? This \u0026rsquo;training data\u0026rsquo; (especially the pre-training data) comes from books, code repositories, and—you guessed it—large collections of website data. Remember that Reddit post you read that contained some really terrible health advice? Yes, posts that might have made its way in there.\nThe good news is that, for most everyday questions, the odds that one random bad Reddit post will completely skew an LLM’s answer is relatively low. These models are trained on massive amounts of text, so any single piece of non-credible information is usually drowned out by thousands of higher-quality examples. In that sense, they “average over” lots of sources and tend to reproduce the most common, consistent patterns they’ve seen.\nThat said, if misinformation is widespread, if a topic is niche or newly emerging, or if the training data underrepresents certain perspectives, the model’s output can absolutely be biased, incomplete, or just wrong. This is especially important in fields like LLMs in doctors\u0026rsquo; Electronic Health Records (EHRs), where bias from common language and text corpora may skew how medical information is \u0026lsquo;interpreted.\u0026rsquo;\nSo, just because these LLMs might often be correct, they statistically will never always be correct, because they are inherently trained on data with varying levels of credibility.\nA simple method for checking information As I reflect on this, I realize that the solution to this quandary may not be much different from the lessons I learned in 6th grade science class. Like Wikipedia, these LLMs are tools. And, like Wikipedia, these LLMs should not be the end-all-be-all for our research on any topic.\nThe solution to this quandary may not be much different from the lessons I learned in sixth-grade science class.\nThe simple solution? Click through the link. Investigate the sources directly.\nMany of the Modern LLMs offer sources with their answers. For example, Google\u0026rsquo;s AI Overview has links, and ChatGPT will often conduct \u0026lsquo;Web Browsing\u0026rsquo; and give links to cite its sources of information.\nSimply clicking through to these links to check a claim by AI information is a great way to get a better idea of:\nwhether the source is credible/to what degree is the source credible (e.g. is it PubMed, or Quora?), and whether the information seems grounded in logic, evidence, and reasoning, or emotion, opinion, and ulterior motives. So, just like how I discovered there was more nuance and gray area between \u0026ldquo;Don\u0026rsquo;t use Wikipedia\u0026rdquo; and \u0026ldquo;Trust everything Wikipedia says,\u0026rdquo;\nI\u0026rsquo;m consistently discovering that there is more nuance and gray area between \u0026ldquo;Don\u0026rsquo;t use AI\u0026rdquo; or \u0026ldquo;Use AI for everything.\u0026rdquo; And, I\u0026rsquo;m consistently discovering that AI functions best as a research tool and a writing assistant, not as an entire researcher and writer.\nReferences McKinsey \u0026amp; Company (2025). New front door to the internet: Winning in the age of AI search\nPew Research Center (2025). Google users are less likely to click on links when an AI summary appears in the results\nAI transparency note In the interest of transparency regarding AI, 95% of this article was written by me and me alone. However, I leveraged ChatGPT to help me with a few paragraphs about how LLMs are trained and how they calculate outputs, because this is not my wheelhouse.\n","permalink":"https://newwordwall.com/journal/evaluating-credibility-era-of-ai/","summary":"\u003cp\u003eWhen I was in 6th grade, we often took \u0026rsquo;trips\u0026rsquo; to the computer lab during science class. Towards the beginning of the year, we were conducting an ecological report on an animal of our choosing, and our teacher Mrs. Blake taught us a lesson about finding credible sources and citing them. Here were the basic tenants I learned:\u003c/p\u003e\n\u003cp\u003e✔️ Whenever possible, reference .edu and .gov sources\u003c/p\u003e\n\u003cp\u003e❌ Do not cite Wikipedia as your only source\u003c/p\u003e","title":"How will we evaluate the credibility and 'correctness' of information in the era of AI? The answer isn't so different from the past..."},{"content":"Linguistic Landscapes Language in Culture and Community Spoken language has consistently proven a difficult entity to research because it——like a cultural value——is highly untangible.\nThis intangible component of society is referred to as a \u0026lsquo;mentifact.\u0026rsquo;\nMentifact (noun): the intangible, ideological components of a culture such as its shared ideas, values, beliefs, attitudes, preferences, and knowledge. A mentifact is one of three subcategories that can be combined to sum up the traits of a culture (Three Components of Culutre). These three components of culture include mentifacts, sociofacts, and artifacts.\nMethods have been developed to study spoken language, but all of these methods involve quantifying the concrete, physical component of spoken language, which is sound, but sound is transient and bound completely to the construct of time. There is, however, a more persistent manifestation of language: the written word.\nWriting, unlike spoken language, is an artifact of society. It is tangible. It can stay static across time, and it can quite literally be held, etched, and erased.\nThe sum of the writing in an environment is referred to as a linguistic landscape.\nLinguistic landscapes are often studied because——with the proper analysis——they are reflections of the diversity, culture, practices, perceptions, and values of a society. Linguistic landscapes are defined as \u0026ldquo;\u0026lsquo;The language of public road signs, advertising billboards, street names, place names, commercial shop signs, and public signs on government buildings combines to form the linguistic landscape of a given territory, region, or urban agglomeration\u0026rsquo;\u0026rdquo; (Landry \u0026amp; Bourhis,1997, p. 25, qtd. in SemiotiX, 2020).\nMuch reseach has been done into linguistic landscapes, and much research has been done into humanistic geography and the distribution of spoken language, but less research has been done into geosemiotics and geographical distributions of written language. Although linguistic landscapes likely function differently from spoken language, there are many parallels to be drawn. This paper proceeds with the hopefully not too optimistic assumption that linguistic theories that have been applied exclusively to spoken language are at least somewhat applicable to the patterns and behaviors of linguistic landscapes.\nLLBert: Linguistic Landscape Coordinate Regressor LLBert is an experiment in using natural language processing to estimate the geographic coordinates associated with text found on signs. The project README documents its data, model, workflow, and interactive predictor.\nLLBert: Linguistic Landscape Coordinate Regressor This project fine-tunes a sentence-transformer model to predict a latitude and longitude pair from text observed on signs at an intersection.\nThe name LLBert is shorthand for Linguistic Landscape BERT (and, conveniently, Latitude Longitude BERT), a playful reference to the inputs and outputs of the sentence-transformer encoder at the center of the project.\nThe model is an experiment in predicting geographic coordinates from linguistic Landscape data, or the language people encounter in public space.\nWhat is a Linguistic Landscape? In simple terms, a linguistic landscape is the sum of all of the written language in an environment.\nWriting, unlike spoken language, is an artifact of society. It extends across time.\nThe sum of the writing in an environment (especially a public environment) is referred to as a linguistic landscape.\nLinguistic landscapes are often studied because—with the proper analysis—they are reflections of the diversity, culture, practices, perceptions, and values of a society.\nLinguistic landscapes can be further defined as:\n\u0026ldquo;\u0026lsquo;The language of public road signs, advertising billboards, street names, place names, commercial shop signs, and public signs on government buildings combines to form the linguistic landscape of a given territory, region, or urban agglomeration\u0026rsquo;\u0026rdquo; (Landry \u0026amp; Bourhis,1997, p. 25, qtd. in SemiotiX, 2020).\nAs an example, let\u0026rsquo;s look at an image of this sign from the University of Colorado Anschutz:\nThere is a lot of data, or information, in this photo. Here, we can see the sign itself, pavement, a mulched garden bed, a road, buildings in the back, and more.\nBut if we were to ask: \u0026ldquo;what linguistic landscape data is in this image?\u0026rdquo; we could likely transcribe it as:\nCU University of Colorado ANSCHUTZ MEDICAL CAMPUS ANSCHUTZ HEALTH SCIENCES BUILDING 1890 NORTH REVER COURT\nSo, the \u0026ldquo;linguistic landscape\u0026rdquo; data of this view of this region contains that information.\nFor further clarification, let\u0026rsquo;s look at another sign on the building:\nHere, we can read two pieces of linguistic data. First, there is the sign itself, which reads:\nANSCHUTZ HEALTH SCIENCES BUILDING\nThen, although they are flipped in orientation, there are the two exit signs, which read:\nEXIT EXIT\nThere is also a subtle reflection of text that appears to read\nANSCHUTZ HEALTH SCIENCES VATORS\nWhich presumably mentions something about the elevators.\nSo, if we were to hypothetically say this was all of the linguistic landscape text outside this building (it\u0026rsquo;s not, but for example), we could say the sum of the linguistic landscape outside of this building was:\nCU University of Colorado ANSCHUTZ MEDICAL CAMPUS ANSCHUTZ HEALTH SCIENCES BUILDING 1890 NORTH REVER COURT ANSCHUTZ HEALTH SCIENCES BUILDING EXIT EXIT ANSCHUTZ HEALTH SCIENCES VATORS\nNotice that there is repetition, and that is good information. Repetition is very common in languauage.\nMuch reseach has been done into linguistic landscapes, and much research has been done into humanistic geography and the distribution of spoken language, but less research has been done into geosemiotics and geographical distributions of written language.\nThis project aims to better understand whether a Machine Learning method of Natural Language Processing (NLP) can accurately predict a geographical coordinate set (latitude, longitude) based on the linguistic landscape data surrounding an intesrsection.\nFor the time being, the project will focus on the greater Denver Metro Area.\nInspiration for Project In high school, I loved playing a game called Hostage. The game began with two teams, with one designated home base.\nEach team would have two sets of people: drivers/navigators, and hostages.\nAt the beginning of the game, you would set a timer for generally 10-20 minutes. Each team would have that much time to take the other team\u0026rsquo;s players to an unfamiliar location and drop them off.\nThe dropped-off hostages then had to work together to figure out where they were and, without using their GPS apps, call their drivers/navigators and explain to them ahd to get to them to pick them up.\nWhichever team made it back to home base with their full team first won.\nThe game turned navigation into a kind of collaborative investigation. Street names, businesses, and public signs were some of the most useful clues because they gave us concrete information about our surroundings. A road sign could tell us the name of a place, a business could identify a neighborhood or intersection, and a cluster of signs could suggest the language, culture, or commercial character of an area. I was relying on the linguistic landscape around me to orient myself, even though I did not have that vocabulary for the process at the time.\nMore recently, while playing the GeoGuessr app, I realized that one of my primary methods for predicting my location was also based on linguistic landscape data. I would look at street names, storefronts, advertisements, public notices, and other written signs, then combine those observations with the visual character of the surrounding area. Recognizing this pattern made me reflect on my own cognitive processes: how was I turning fragments of written language into a geographic intuition?\nThese experiences led me to wonder whether linguistic data alone could be enough to predict geographical location.\nLLBert is an attempt to investigate that question computationally. Rather than treating the model only as a geolocation system, I see it as a model of cognitive analysis: a way to examine whether the kinds of linguistic clues people use to orient themselves can be represented, learned, and used to estimate where a scene might be located.\nModel Training Data The raw dataset is expected at training_data_raw.csv. It may include the pandas-exported index column; the preprocessing script only uses:\nintersection text_on_sign_exact latitude longitude The project can also build this raw dataset directly from the source spreadsheets. Put the exported workbooks in ll_sheets/ and place the intersection coordinate lookup at coordinate_dict10.xlsx. The lookup\u0026rsquo;s DMS coordinate strings are converted to decimal latitude and longitude during aggregation.\nWorkflow From source spreadsheets Run the complete data-preparation flow from the project root:\nsource .venv/bin/activate python prepare_training_data.py \\ --from-ll-sheets \\ --sheet-dir ll_sheets \\ --coord-file coordinate_dict10.xlsx \\ --input-file training_data_raw.csv \\ --output-file training.csv This command reads every .xlsx file in ll_sheets/, cleans and combines the sign records, joins each intersection to coordinate_dict10.xlsx, and writes the two training stages:\ntraining_data_raw.csv: cleaned sign-level records with coordinates. training.csv: deterministic bootstrap bags used by the model trainer. The default command creates 100 samples per intersection, with 8 sign texts in each sample. Use --bag-size, --samples-per-intersection, and --seed to change those settings. To reuse an existing raw CSV instead, omit --from-ll-sheets and run the preparation command below.\nPrepare bootstrapped training rows:\nsource .venv/bin/activate python prepare_training_data.py --seed 1992 --bag-size 5 --samples-per-intersection 50 This writes training.csv with rows shaped like:\nintersection,sample_id,text,latitude,longitude,unique_sign_count,raw_sign_count Each row is a deterministic bootstrap sample of sign texts from one intersection, joined into a single text field. This trains on \u0026ldquo;some signs seen at this coordinate\u0026rdquo; instead of one sign or every sign at that coordinate.\nTrain the model:\npython train.py Evaluate and write predictions:\npython eval.py This writes predictions.csv and a map-style diagnostic plot:\nIt also writes a coordinate calibration plot:\nOr run the full pipeline:\nmake Try the model in a web app Once output/ contains a trained model, launch the interactive predictor:\nmake serve Open prediction lets you enter a bundle of sign text, provide your own latitude and longitude estimate, and compare it with LLBert\u0026rsquo;s prediction. The Random round mode selects a held-out-style row from training.csv, hides its coordinates, and scores both your guess and the model. The frontend is plain HTML and JavaScript, served by FastAPI, so it can be embedded directly in Hugo or deployed as a small app on its own.\nHosting and Hugo embedding The recommended architecture is a static Hugo site plus this small FastAPI service. You have two deployment options:\nDirect Hugo integration: copy web/index.html into a Hugo page or shortcode, and set window.LLBERT_API_URL before the app script to the API\u0026rsquo;s HTTPS URL. Subdomain: deploy the whole app and API together at something like llbert.example.com, then embed that URL in Hugo with an iframe. For a first deployment, Render or Fly.io are a better fit than a static host because the model needs Python, PyTorch, and a long-lived process:\nPush this repository to GitHub, including app.py, web/, requirements.txt, and the trained output/ directory. Configure the service command as uvicorn app:app --host 0.0.0.0 --port $PORT. Set LLBERT_ALLOWED_ORIGINS to the Hugo site\u0026rsquo;s origin when the frontend is hosted separately. runtime.txt selects Python 3.10. For a Hugo page, the smallest embed is:\n\u0026lt;iframe src=\u0026#34;https://llbert.example.com\u0026#34; title=\u0026#34;LLBert linguistic landscape coordinate predictor\u0026#34; loading=\u0026#34;lazy\u0026#34; style=\u0026#34;width:100%; min-height:900px; border:0;\u0026#34; \u0026gt;\u0026lt;/iframe\u0026gt; The app reads LLBERT_MODEL_PATH, LLBERT_DATA_PATH, and LLBERT_ALLOWED_ORIGINS when set, which is useful for a container or another host. Keep the model service separate from Hugo: Hugo is static, while prediction requires Python, PyTorch, and the sentence-transformer model. The model\u0026rsquo;s predictions are approximate and should not be treated as precise geolocation.\nCommand-line options and defaults All of the project scripts accept command-line flags, and each one has a default value that is used when you do not pass an override. The defaults are intentionally documented here so it is clear which behavior can be customized.\nprepare_training_data.py Creates the bootstrapped training CSV from the raw sign data.\nArgument Default Description -i, --input-file training_data_raw.csv Raw pandas-exported CSV to read. -o, --output-file training.csv Prepared training dataset written by the script. --from-ll-sheets False Build the raw input from all .xlsx files in --sheet-dir and the coordinate workbook. --sheet-dir ll_sheets Directory containing source spreadsheet exports. --coord-file coordinate_dict10.xlsx Intersection-to-coordinate lookup workbook. --seed 1992 Random seed used for deterministic bootstrapping. --bag-size 8 Number of sign texts to combine into each training row. --samples-per-intersection 100 Number of bootstrap bags generated for each intersection. --separator `\u0026quot; \u0026ldquo;` Example:\npython prepare_training_data.py \\ --input-file training_data_raw.csv \\ --output-file training.csv \\ --seed 1992 \\ --bag-size 5 \\ --samples-per-intersection 50 \\ --separator \u0026#34; | \u0026#34; To rebuild both files from the source spreadsheets, use:\npython prepare_training_data.py \\ --from-ll-sheets \\ --sheet-dir ll_sheets \\ --coord-file coordinate_dict10.xlsx train.py Trains the sentence-transformer encoder and coordinate regression head.\nArgument Default Description --data-file training.csv Training dataset path. --output-path output Directory where model artifacts are written. --model-name sentence-transformers/all-MiniLM-L6-v2 Base sentence-transformer model to fine-tune. --device None Optional explicit device such as cpu, cuda, or mps; if omitted, CUDA is used when available and otherwise CPU is used. --seed 1992 Random seed for reproducibility. --epochs 10 Number of training epochs. --batch-size 64 Batch size for training. --num-workers 2 DataLoader worker processes. --save-every-epochs 5 Save the best checkpoint every N epochs (and at final epoch). --learning-rate 1e-4 Learning rate for the encoder. --head-learning-rate 5e-2 Learning rate for the coordinate head. --weight-decay 0.001 Weight decay for optimization. --test-size 0.2 Fraction of rows used for the validation/test split. --hidden-dim 256 Hidden dimension in the coordinate regressor. --dropout 0.1 Dropout used inside the regression head. --freeze-encoder False If set, only the coordinate head is trained and the encoder stays fixed. --freeze-transformer-layers 0 Freeze the first N transformer layers in the encoder. --freeze-attention False Freeze self-attention parameters while leaving other encoder parameters trainable. Example:\npython train.py \\ --data-file training.csv \\ --output-path output \\ --model-name sentence-transformers/all-MiniLM-L6-v2 \\ --device cuda \\ --epochs 20 \\ --batch-size 32 \\ --learning-rate 2e-5 \\ --head-learning-rate 1e-2 \\ --hidden-dim 512 \\ --dropout 0.15 eval.py Loads a trained model and writes predictions and diagnostics.\nArgument Default Description --data-file training.csv Dataset to score. --model-path output Directory containing the trained model artifacts. --output-file predictions.csv CSV path for predicted coordinates and error metrics. --plot-file plots/prediction_map.png Path to the map-style prediction error plot. --scatter-plot-file plots/predicted_vs_actual.png Path to the predicted-vs-actual scatter plot. --device None Optional explicit device override; otherwise CUDA is used when available, else CPU. --batch-size 64 Batch size used for inference. --seed 1992 Must match the training seed for the same test split. --test-size 0.2 Must match the training split size. Example:\npython eval.py \\ --data-file training.csv \\ --model-path output \\ --output-file predictions.csv \\ --plot-file plots/prediction_map.png \\ --scatter-plot-file plots/predicted_vs_actual.png \\ --batch-size 128 generate_data.py Generates city-distance data used for computing distances between cities.\nArgument Default Description -c, --country US Country code to use when searching cities. -w, --workers 1 Number of worker threads used for computation. -s, --chunk-size 1000 Batch size for chunking distance calculations. -o, --output-file distances.csv Output CSV path for generated distances. --shuffle False If set, shuffle the combinations before processing. Example:\npython generate_data.py \\ --country US \\ --workers 4 \\ --chunk-size 2000 \\ --output-file distances.csv \\ --shuffle Outputs training.csv: prepared bootstrapped dataset. output/: saved sentence-transformer encoder, coordinate head, and coordinate normalization metadata. predictions.csv: evaluation rows with predicted coordinates and error_km. plots/prediction_map.png: actual vs predicted coordinates with line segments showing the prediction error. plots/predicted_vs_actual.png: predicted vs actual latitude and longitude scatter plots. References Ernst, P. (1969). The tongues of Italy: prehistory and history. Greenwood Press.\nGoogle (2020). Google Maps. Google. https://www.google.com/maps\nHuisman, J. L. A., Majid, A., \u0026amp; van Hout, R. (2019). The geographical configuration of a language area influences linguistic diversity. Public Libary of Science. 14(6).\nMetro Denver (2020). Communities. MetroDenver Economic Development Coorporation. *http://www.metrodenver.org/do-business/communities/\nSemiotiX (2020). The sociolinguisics of space and semiotic landscapes: An introduction. SemiotiX: A global information magazine. https://semioticon.com/semiotix/2013/05/the-sociolinguistics-of-space-and-semiotic-landscapes-an-introduction/\nTrudgill, P. (2000). “Chapter 5: Language and Context,” Sociolinguistics: An introduction to language and society. (4th ed., pp. 81-104). Penguin Books.\n","permalink":"https://newwordwall.com/projects/linguistic-landscapes/","summary":"\u003ch1 id=\"linguistic-landscapes\"\u003eLinguistic Landscapes\u003c/h1\u003e\n\u003ch2 id=\"language-in-culture-and-community\"\u003eLanguage in Culture and Community\u003c/h2\u003e\n\u003cp\u003eSpoken language has consistently proven a difficult entity to research because it——like a cultural value——is highly untangible.\u003c/p\u003e\n\u003cp\u003eThis intangible component of society is referred to as a \u0026lsquo;mentifact.\u0026rsquo;\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003eMentifact (noun): the intangible, ideological components of a culture such as its shared ideas, values, beliefs, attitudes, preferences, and knowledge. A mentifact is one of three subcategories that can be combined to sum up the traits of a culture (\u003cem\u003eThree Components of Culutre\u003c/em\u003e). These three components of culture include mentifacts, sociofacts, and artifacts.\u003c/p\u003e","title":"Linguistic Landscapes"},{"content":"Definition Compulsively scrolling through negative or anxiety-inducing news or social media content.\nOrigin The term emerged during the COVID-19 pandemic and became associated with excessive digital media consumption.\nExample “I stayed up until 2 AM doomscrolling TikTok and news feeds.”\nRelated Terms digital fatigue algorithmic anxiety information overload ","permalink":"https://newwordwall.com/words/doomscrolling/","summary":"\u003ch2 id=\"definition\"\u003eDefinition\u003c/h2\u003e\n\u003cp\u003eCompulsively scrolling through negative or anxiety-inducing news or social media content.\u003c/p\u003e\n\u003ch2 id=\"origin\"\u003eOrigin\u003c/h2\u003e\n\u003cp\u003eThe term emerged during the COVID-19 pandemic and became associated with excessive digital media consumption.\u003c/p\u003e\n\u003ch2 id=\"example\"\u003eExample\u003c/h2\u003e\n\u003cblockquote\u003e\n\u003cp\u003e“I stayed up until 2 AM doomscrolling TikTok and news feeds.”\u003c/p\u003e\u003c/blockquote\u003e\n\u003ch2 id=\"related-terms\"\u003eRelated Terms\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003edigital fatigue\u003c/li\u003e\n\u003cli\u003ealgorithmic anxiety\u003c/li\u003e\n\u003cli\u003einformation overload\u003c/li\u003e\n\u003c/ul\u003e","title":"Doomscrolling"},{"content":"Singaid (idiom, idiomatic expression) Pronunciation /siŋ-eɪd/ or /sɪŋ-eɪd/\nsing as in \u0026ldquo;sing\u0026rdquo; or \u0026ldquo;sin\u0026rdquo; with a g (depending on dialect) aid as in \u0026ldquo;aid\u0026rdquo;—offering help.\nDefintion Said to someone who has just hiccuped, as a polite remark; a remark offering good wishes of hiccups passing.\nLike the English expression bless you, which is often used when someone is sneezing, singaid is a polite expression towards someone with the hiccups.\nUse of the word singaid person one: *has the hiccups*\nperson two: \u0026ldquo;singaid\u0026rdquo;\nperson one: *hiccup* \u0026ldquo;Thank you so much——\u0026rdquo; *hiccup* \u0026ldquo;I hate the——\u0026rdquo; *hiccup* \u0026ldquo;hiccups——\u0026rdquo; *hiccup*\nEtymology and Word History Singaid is derived from:\nsing-, an affix that comes from the medical term for hiccups, singultus, which is Latin for gasp or sob; and -aid, a word that comes from the English word aid, meaning to provide support, assistance, or help Combined, singaid acts as a wish of aid when someone has the hiccups.\n","permalink":"https://newwordwall.com/words/singaid/","summary":"\u003ch1 id=\"singaid-idiom-idiomatic-expression\"\u003e\u003cstrong\u003eSingaid\u003c/strong\u003e (\u003cem\u003eidiom, idiomatic expression\u003c/em\u003e)\u003c/h1\u003e\n\u003ch2 id=\"pronunciation\"\u003ePronunciation\u003c/h2\u003e\n\u003cp\u003e/siŋ-eɪd/ or\n/sɪŋ-eɪd/\u003c/p\u003e\n\u003cp\u003e\u003cem\u003esing\u003c/em\u003e as in \u0026ldquo;sing\u0026rdquo; or \u0026ldquo;sin\u0026rdquo; with a g (depending on dialect)\n\u003cem\u003eaid\u003c/em\u003e as in \u0026ldquo;aid\u0026rdquo;—offering help.\u003c/p\u003e\n\u003ch2 id=\"defintion\"\u003eDefintion\u003c/h2\u003e\n\u003cp\u003eSaid to someone who has just hiccuped, as a polite remark;\na remark offering good wishes of hiccups passing.\u003c/p\u003e\n\u003cp\u003eLike the English expression \u003cem\u003ebless you\u003c/em\u003e, which is often used when someone is sneezing, \u003cem\u003esingaid\u003c/em\u003e is a polite expression towards someone with the hiccups.\u003c/p\u003e\n\u003ch2 id=\"use-of-the-word-singaid\"\u003eUse of the word \u003cstrong\u003esingaid\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eperson one: *has the hiccups*\u003c/p\u003e","title":"Singaid"}]