Boolean Explained

Let’s take a dive into the engagingWith: operator… Firstly, let’s get some context: author: captures posts by a person engagingWith: captures replies and shares of posts by a personYou can use engagingWith:rihanna to measure engagement as people respond to (or retweet) a post from the account, even if they don’t mention the word rihanna in the post. Mentions will be indexed when people have replied or Retweeted a post from the specified account. For Twitter, engagingWith: only looks at the immediate parent, so it will not pull all replies where there is a large tree of replies and sub-replies. For Reddit we have engagingWithUrl: which is always the root post of the comment chain, e.g. all comments and nested comments within comments will be found with the engagingWithUrl: operator querying the url of the original post.engagementType: is a refinement which you can add on, for example if you only wanted comments and quotes, not retweets, you could do engagingWith:rihanna NOT engagementTy

Hi Everyone… I’ve written a quick and short article on AI Entity Searches following a couple of recent discussions with the aim of helping you understand how it works and ultimately appreciate the incredible amounts of time it can save and value it can deliver because it goes further than we can ourselves. What is Entity Search? AI entity search takes into consideration the definition of the terms that you are searching for.Say for example that you wanted to search for the brand Apple. A manual keyword search only containing the word "apple" would also return mentions of fruits and any other apple based product names or brand names, so you would need to add additional context to your search in order to get to the relevant content (e.g. Apple AND (iphone OR ipad… etc)). However, using AI-powered entity search, you pick the concept that you are searching for — in this case the brand Apple, not the fruit — and the system will return mentions of the entity that you have selected.Noun extra

Hello! A quickish one this month, and perhaps one you’ve not thought about. We’re going to look at how to leverage Consumer Research to identify Owned and Earned media and what this can tell us about your content strategy.Owned Media Earned Media Channels and platforms that a brand creates and controls, such as a website, blog, social media profiles, and email marketing lists.  Publicity or exposure that a brand gains through organic sources, like reviews, mentions, shares, or news coverage.   I’ve used Ryanair as the example here and broken down a basic query into Owned and Earned by using particular operators which lend themselves to each category. Owned Mediaauthor:ryanair OR ryanair_es OR ryanair_it OR ryanair__frOR title:ryanairOR site:ryanair.comOR (site:twitter.com AND (author:ryanair OR ryanair_es OR ryanair_it OR ryanair__fr))OR (site:instagram.com AND author:ryanair)OR (site:facebook.com AND author:ryanair)OR (site:youtube.com AND title:{ryanair} AND author:

Hi everyone,Updated: 11th August 2025TikTok in Consumer Research Handling multiple TikTok accounts Setting up Alerts for viral TikTok posts Useful operators for segmentation Location data Links to TikTok Comprehensive TikTok Query How does TikTok content propagate on the wider web? Query level TikTok links Influencer tracking via links Cleverly utilise #*Tok to identify trends. Hashtags TikTok Trend Identification (Reddit) Dashboard level Topic Clouds Top Subreddits  TikTok in Consumer ResearchFirstly, and perhaps most excitingly: TikTok date is available in Brandwatch Consumer Research, as of February 28th 2025.The API is constantly evolving - so expect more updates throughout this year.For now you can expect to receive:Top 1,000 brand mentions (based on number of likes received) from posts made within the past 90 days Top 1,000 comments mentioning the client’s brand, posted in the past 90 days Up to one year's owned data, including comments on those posts.The following metr

Let’s take a deep dive in to the Site, URL and Link operators. If you have anything to add, please let a comment below to share it with the community! site: site:bbc.comOnly brings in mentions from specific sites The indexed site field is populated with one level past the top public suffix. Since .com is a public suffix and pcmag is private, au.pcmag.com receives pcmag.com as its site. The common example for when subdomains are present in the site is myblog.blogspot.com: blogspot.com allows public registries so we preserve the subdomain. The workaround for this particular example is to exclude url , eg. NOT (url:"http://pcmag.com" OR url:"https://pcmag.com") This command can be helpful when looking for a particular piece of content on a given website.  site:bbc.Matches all documents from a site and its affiliated subdomain sites eg https://www.bbc.com/, https://www.bbc.co.uk, etc.  URL: URL:bbc.comURL captures any part of the url so if you do url:bbc you will get bbc.com but also bbc.

Hi allThis month, maybe the start of a new vein of Boolean Explained. We’re going to explore some dashboard components, starting with the Topic Cluster.​@Melissa Flanjack says “The Topic Cluster is an underrated component” and let me tell you… she’s knows what she’s talking about!Here’s why Topic Clusters is an underrated component:Switching the Topics to "Verbs" and Subtopics to "Adjectives" is a great way to understand into how consumers are feeling about a given topic. It typically works well for unbranded topic queries and is REALLY helpful if filtered to Reddit specifically.This is an example for a query of people who have Alopecia and shows how the clusters tap into their experience and feelings around their Alopecia. Bonus points for segmenting by Categories.This is the same query but segmented by categories set up around impacts on daily life (specifically filtered to confidence and relationships). Now we’re tapping into how different aspects of patients' daily lives are effect

Hello everyone. A much requested one this month -- a breakdown of Purchase Intent. What does that mean?Well, its all about breaking down the pre and post purchase intent, the difference between thinking of and wanting to purchase something, vs those who have already purchased and own the product or service.From this data, you can begin to understand and analyse consumer opinions in your dashboards, which brings me to…..Coupled with this article is an exclusive live training webinar with ​@Ed Chapman on Monday 28th or Thursday 31st July. If you have any questions after reading this (You probably will!) then the webinar is the perfect place to put them to ​@Ed Chapman, who will show you first hand how to set this up in your dashboard and bring consumer opinions to life.Register for free and log in to Brandwatch Academy. If you can’t make either time of the webinar, register anyway and you will get the link to watch it back. In this article we’ll focus largely on the Boolean aspect, which

Hi allThis month, I’ve got a very useful tip for you all on identifying paid media, specifically social media ads.Before reading on, this article might also help inform your understanding here and even help you build a dashboard to help you understand Owned, Earned and now… Paid Conversation.You may also find this article useful, explaining the Links operator and some differences between it and the URL and Site operators.The tip is simply: use UTM links, with a unique campaign name (or other easily identifiable field) eg.https://community.brandwatch.com/?utm_medium=owned_social&utm_source=community&utm_campaign=communitytestlink&utm_content= Here’s a test tweet I put out using the above UTM link. I then wrote a simply query to find it: And here it is in my dashboard: And… that’s it!  In principle you can take this tip and apply it to owned content as well as paid for earned content (specifically that you can “manage” e.g. influencers).If you refer to the article linked abov

Let’s take a deep dive in to the location specific operators. If you have anything to add, please let a comment below to share it with the community! When we have location data, either geotagged or inferred, we classify mentions by location.Location data is not available across all content sources, often based on platforms/pages terms & conditions. We do not infer location for the following sites, among others:Instagram & Facebook Reddit YouTube QQ  Data CollectionIf your query needs to be restricted to one location use the country operator within your Boolean instead of using the location filter. And make sure to include FB & IG with the site operator.(( “Taylor Swift” OR TaylorSwift OR @taylorswift OR @taylorswift13 )AND (country:”GBR” OR site:(facebook.com OR instagram.com))) Additionally, you can use context keywords to target a specific location.(( “Taylor Swift” OR TaylorSwift)NEAR/20 (“united kingdom” OR unitedkingdom OR UK OR british OR britain OR england OR scotlan

Hi allJust wanted to share this little query string from ​@Alec Gillies in case you find it useful. Any suggested amendments are welcome to make it even more comprehensive. Please reply with them below and I can update this post. Anyone doing so will unlock the Query Builder badge! The RED TEXT is to be replaced with your Brand name, @handle, Channel ID etc. OR (“BRAND NAME” NEAR/5 RELEVANT BRAND TERM)OR @BRANDOR site:x.com/BRANDOR site:facebook.com/1234567891011 (CHANNEL ID)OR channelId:1234567891011(CHANNEL ID)OR (site:instagram.com AND author:BRAND)OR site:youtube.com/BRANDOR #BRANDHASHTAG OR site:BRAND.COMOR links:aBRANDOR url:BRANDOR title:(“BRAND” OR “ALTERNATE BRAND NAME”)

This month I’m trying something a bit different with Boolean Explained. Getting great results still relies on a well-crafted query, of course, which is important here. But we’re going to look a bit more into the story telling, dashboards and widgets, to understand how a company might assess potential investment in influencers / brand ambassadors etc. through 2 use cases. Use Case 1: What contribution has X, Y, Z influencer had on our brand / brand campaign?Use Case 2: Using BW to conduct due diligence on influencers Academy Trainer ​​​​​@Ed Chapman will be hosting a one-off live training specifically on the topic of leveraging Consumer Research to demonstrate the ROI of influencers and how to execute due diligence as discussed below.Sign up here → Thursday May 29th, 3pm GMTUse Case 1:The first thing to do is introduce the context of my example. Presenting, Liam Gallagher “BIBLICAL/CELESTIAL/MAJESTICAL/APPROACHABLE/HUMBLE/SPIRITUAL” rock n’ roll star and frontman of Oasis. Back in Octob

An often overlooked step in creating a kick-ass query is arguably the most important one, and it’s about getting back to basics. When did you last Google search your own brand?If you are lucky enough to be searching for an unambiguous brand then you can often get away with quite a simple Query string that will not require too much syntax. IKEA is a favourite example of a unique brand term that captures 99% relevant mentions with just one term in the string (the other 1% is for the Scandinavian furniture enthusiasts that use IKEA in profile names, and a song about everyone’s favourite flat pack furniture company, by Jonathan Coulton on his Album Smoking Monkey).… Or maybe you have a more challenging brand name. SEAT, NEXT and MAC (cosmetics).Google the brand, note any similar names that aren't relevant for your exclusion list. Eg with Penfield (the outdoor clothing company) a Google search throws up: Wilder Penfield (a famous researcher), Penfield Marine (an oil tanker company), Penfiel

Hi everyone. A bit of a different one this month, looking at how to Identify Bots and Spam, as well as filtering this data if you would like to. Filtering these mentions out could make it easier to analyse and build a clearer picture of conversation around your organisation, products and competitors etc.Note: The figures proposed in this article are purely suggestions and highly recommend a period of experimentation in order to ensure you are satisfied with what the tags are filtering out.If you’re interested in this topic then will probably benefit from reading this:And while you’re here, if you have any query exclusion strings of your own which you might like to share with the community - I’ve just added a very useful bot related exclusion string! Adding Filters to TagsLet’s get started. Here are some typical attributes which we recognise in Bot and Spam accounts.Filter Why? Proposed Starting Value Low Follower Volume Bot / Spam Accounts typically have low followers 25 (Max)

Are you bringing in irrelevant mentions? Sometimes key terms need some additional context. The NEAR/ operator will help with that. Let’s take a deep dive…What is the NEAR/ operator?By utilising the NEAR/ operator we can find mentions within a certain number of words from each other. How many words? Your choice, just place a number after the operator to choose the distance between the words its working with. This gives the flexibility to target different word types and sentence structures.Using NEAR/ Using NEAR to understand opinions  NEAR with Japanese language (and other similar languages) Using NEAR/ to understand if there is media contained within posts Finding keywords mentioned multiple times Using NEAR/(((Christmas OR Xmas) NEAR/2 (Gift OR Present))Using Multiple NEAR/ OperatorsNEAR/ is great for adding context, but maybe it's not enough. Try using multiple NEAR/’s. By continuing your search string with another set of NEAR criteria you can continue to refine the search and releva

It’s a been a little while since we took a deep dive into a channel. This time, let’s try Reddit! In case you didn’t know, as of July 2024 Brandwatch has a direct “firehose” into Reddit, offering richer metadata and direct Reddit access. Note: Reddit does not provide Location Data to us. When filtering in your dashboard to certain Locations, Reddit data will not appear. Setting Location as Worldwide is the only way to ensure Reddit data is returned in your Query.  Operators, what you need to know: Operator  Example  Description  subreddit  subreddit: cars AND tesla  Returns all posts and replies belonging to the named subreddits  subredditTopics  AND / OR subredditTitle  subredditTopics: cars   Returns all posts and replies belonging to any subreddit whose title has the keyword “cars” within it. You can use extra boolean here to get more specific.  weblogTitle  weblogTitle:AlexTheGreat  Returns a

Hello Community!We’re taking a look at Review data and some tips and tricks you can use to get the most out of your query. This article will help you find Reviews of your products, services or organisation – on indeed all of the above for competitors and there is a lot to cover so please bear with me. As always, any questions please fire away in the comments and someone from Brandwatch or the Community will hopefully come back to you. We collect Mentions from a range of Review sites including Google Play, Apple App Store, OpenTable, Flipkart, Steam Community, Best Buy, Glassdoor, Review Centre, Reviews.io, eBay, CNET, Amazon, TrustPilot and more, using our third-party data providers. We can also add coverage for particular product reviews upon request. To do this, please contact support@brandwatch.com Full list of included sources:AgodaAvvoExpedia  (Only hotel reviews)Facebook (Only publicly available)GoogleGoogle PlaystoreGoogle ShoppingZomatoAirbnb AliExpress  (.com, .us, .ca. co.uk)

Let’s talk query exclusions and how to filter out data you don’t want. Firstly, we have a dedicated topic for sharing query strings. Have a search in here and please add your own to help the Community. On to the tips and tricks;  Filtering images using Object IDs Explicit content often comes in the form of images on X (Twitter), and cannot always be filtered by Boolean or AI because the text is similar to relevant data you might want to track. As such, we need a solution leveraging metadata instead. To do this we use Object IDs, take a look at this exclusion example for pornographic content: (objects: (3093 <<<active undergarment>>>OR 3388 <<<art model>>>OR 4081 <<<barechestedness>>>OR 4097 <<<banana family>>>OR 3873 <<<bed>>>OR 3930 <<<bedroom>>>OR 4160 <<<bedtime>>>OR 3411 <<<bikini>>>OR 3316 <<<bodybuilding>>>OR 3283 <&l

Hello Community!It’s been a while since we’ve taken a look under the hood at query writing. Well, we’re back with a look at Brackets/Parentheses in our Boolean Explained Series.  Case Sensitive Data The raw operator has been retired in favour of pointed brackets { }. To return case sensitive results, just place the phrase you’re looking for inside the brackets.  Not case-sensitive: British Telecom OR BT Will likely find mentions of both “British Telecom” and “BT:, but might also include misspellings of the word “but” or the acronym for “Bad Trip”, because we haven’t specified that BT must be case-sensitive (which is how references to the brand BT are typically written).Case-sensitive:  British Telecom OR {BT} This query specifies that BT should only be included if it is written in the same case-sensitive format as the word inside the pointed brackets.  Note: Will only retrieve Mentions where upper case letters are used in the exact way specified inside the brackets. Upper case matching

Hi all! The continuing rise in popularity of Generative AI tools like ChatGPT provides an opportunity to augment human creativity and paves the way for optimising strategic planning and decision making. With that in mind, we had to do a deep dive into it...All of the below is connected with using tools outside Brandwatch to help you write killer queries. However, we have AI tools of our own too, which you might already be using. If you are and could do with a refresh or if you’d like to learn how to use them, then head to the bottom of this post for details of an exclusive webinar hosted by @Ed Chapman from our Academy team. Using ChatGPT to help write queriesAsking for help to identify a boolean error  List out different variations of a topicThis is particularly useful when creating categories, e.g different flavours and garnishes of daiquiri to help create categories for a pie chart answering the Q on which flavour/garnish is most spoken about. Quickly understand a topic with concise

Hi allAnother channel special this month, following our popular Tiktok one last month. As always, if you have any tips yourself to share - please do so! And if you’ve found this post useful, show it some love 👍🏼 Note: YouTube Shorts Data is not pulled via our crawlers.Find out more about Youtube mentions and metadata in this Help Center article Find YouTube channels that post about your brandWe’ve got a whole topic dedicated to this one thanks to @Jennifer Wilder If you are interested in tracking comments for a particular video or wish to track all videos and comments posted by a particular YouTube channel, please contact our Support team with the channel IDs. The team will check if we already cover the channel or video in question and if we do not, we will add it to our coverage.Note: We cannot guarantee historical data coverage when indexing new channelsNote: You should also add these channels or videos into your query Search the whole of Youtubesite:youtube.com AND “John Mayer”Not

Hello all! Thought we could take a deep dive into one of our Operators, in this case…. The * Wildcard OperatorThe purpose of the Wildcard operator is to find mentions that include the root of a word. It’s really useful for finding lots of variations on a word, without having to include them all in your query.For example, I could write a query that looks like this:Kiss OR Kisses OR Kissing OR Kissed OR KisserBut using the Wildcard operator, I can shorten that query down to:Kiss*The use of the * here instructs Brandwatch to find any word that contains the root Kiss.  Placement is everything with the Wildcard!Where you place the Wildcard operator in a word has a big impact on the output. For example, if I were to move the * towards the beginning of the word Kiss like this:Ki*Then this instruction tells Brandwatch to find any word that starts with “Ki”. Kids, Kin, Kim, Kissing, Kilogram, Kindling and many more would be captured by this query.Similarly, I can restrict the number of words fo