GNIP Search PowerTrack Rules

 Please refer to GNIP official documentation 

PowerTrack Rules


Introduction

Products utilizing PowerTrack rules deliver social data to you based on filtering rules you set up. Rules are made up of one or more ‘clauses’, where a clause is a keyword, exact phrase, or one of the many PowerTrack Operators.

Multiple clauses can be combined with both ‘and’ and ‘or’ logic. ‘And’ logic is specified with a space between clauses, and ‘or’ logic is specified with an upper-case OR. See below for more details…

Rules can be up to 2,048 characters long with no limits on the number of positive and negative clauses

Limitations of PowerTrack rules for searching within the last 30 days

Building Rules with PowerTrack

Keyword match

Keyword matches are similar to queries in a search interface (e.g. Google). For example, the following PowerTrack rule would match activities with ‘happy’ in the text body.

 happy

ANDing terms with white space

Adding another keyword is the same as adding another requirement for finding matches. For example, this rule would only match activities where both ‘happy’ and ‘party’ were present in the text, in either order – having a space between terms operates as boolean AND logic. Note that if you include an explicit AND it will be treated as a regular keyword.

 happy party

ORing terms with upper-case OR

Many situations actually call for boolean OR logic, however. This is easily accomplished as well. Note that the OR operator must be upper-case and a lower-case ‘or’ will be treated as a regular keyword.

 happy OR party

Negating terms

Still other scenarios might call for excluding results with certain keywords (a boolean NOT logic). For instance, activities with ‘happy’, but excluding any with ‘birthday’ in the text.

 happy -birthday

Grouping with parentheses

These types of logic can be combined using grouping with parentheses, and expanded to much more complex queries.

 (happy OR party) (holiday OR house) -(birthday OR democratic OR republican)

This is just the beginning though – while the above examples rely simply on tokenized matching for keywords, PowerTrack also offers a operators to perform different types of matching on the text.

Exact match

 "happy birthday"

Substring match

 contains:day

Proximity match

 "happy birthday"~3

Further, other operators allow you to filter on unique aspects of social data, besides just the text. For example:

The user who is posting a Tweet

 from:user

Foursquare checkins within 10 miles of Pearl St. in Boulder, CO

 point_radius:[-105.27346517 40.01924738 10.0mi]

These can be combined with text filters using the same types of logic described above.

 (happy OR party) (holiday OR house OR "new year's eve") point_radius:[-105.27346517 40.01924738 10.0mi] lang:en -(birthday OR democratic OR republican)

Before beginning to build PowerTrack rules, be sure to review the syntax described below, look through the list of available operators, and understand the restrictions around building rules. You should also be sure to understand the nuances of how rules are evaluated logically, in the ‘order of operations’ section.


Boolean Syntax

The examples in the previous section, utilized various types of boolean logic and grouping. See the table below for additional detail regarding the syntax and requirements for each.

Logic Type PowerTrack Syntax Description
AND social data Whitespace between two operators results in AND logic between them

Matches activities containing BOTH keywords (‘social’, ‘data’).

Do NOT use AND explicitly in your rule. Only use whitespace. An explicit AND will be treated like a regular keyword.

OR social OR data To OR together two operators, insert an all-caps OR, enclosed in whitespace between them

Matches activities with EITHER keyword (‘social’ OR ‘data’)

Note that if you combine OR and AND functionality in a single rule, you should understand the order of operations described here, and consider grouping operators together using parentheses as described below to ensure your rule behaves as expected.

You must use upper-case ‘OR’ in your rule. Lower-case ‘or’ will be treated as a regular keyword.

NOT social -data
apple -(fruit OR orange)
apple -(android phone)
Insert a – character immediately in front of the operator or group of operators.

The example rule shown matches activities containing keyword ‘social’, but excludes those which contain the keyword ‘data’)

Negated ORs are not allowed where the rule would request “everything in the firehose except the negation.” E.g., apple OR -ipad is invalid because it would match all activities except those mentioning ‘ipad’.

Grouping (social OR data) -(gnop OR ping) Parentheses around multiple operators create a functional “group”.

Groups can be connected to clauses in the same manner as an individual clause via whitespace (AND) or ORs, and can be negated. However, note that the same restriction described above regarding negation/OR combination also applies to groups. For example, the following are examples of invalid syntax using groups:
ipad OR -(iphone OR ipod)
ipad OR (-iphone OR ipod)

Grouping is especially important where a single rule combines AND and OR functionality, due to the order of operations used to evaluate the rule. See below for more details.

Note that Operators may be either positive or negative.

Positive Operators define what you want to include in the results. E.g. the ‘has:links’ operator says “I want activities containing URLs.”

Negative Operators define what you want to exclude from the results, and are created by using the Boolean NOT logic described above. E.g. ‘-has:links’ says “Exclude any activities containing URLs, even if they otherwise match my rule.”

A single PowerTrack rule can support up to 30 positive operators, and up to 50 negative operators, subject to the restrictions documented here.


Order of Operations

When combining AND and OR functionality in a single rule, the following order of operations will dictate how your rule is evaluated.

  1. Operators connected by AND logic are combined first
  2. Then, Operators connected with OR logic are applied

Example:

  • apple OR iphone ipad would be evaluated as apple OR (iphone ipad)
  • ipad iphone OR android would be evaluated as (iphone ipad) OR android

To eliminate uncertainty and ensure that your rules are evaluated as intended, group terms together with parentheses where appropriate. For example:

  • (apple OR iphone) ipad
  • iphone (ipad OR android)

Punctuation, Diacritics, and Case Sensitivity

In PowerTrack Operators, characters with accents or diacritics are treated the same as normal characters and are not treated as word boundaries. For example, a rule of cumpleaños would only match activities containing the word cumpleaños and would not match activities containing cumplea, cumplean, or os.

All Operators are evaluated in a case-insensitive manner. For example, the rule Cat will match all of the following :cat, CAT, Cat.


List of Operators

Below is a list of all operators supported in PowerTrack. Note that while many operators work across multiple different data sources, others are specific to a specific source. See the Sources column for the data sources that a specific operator applies to.

Or, for a list of all the operators available for a specific source, see one of the following links.

keyword Matches a keyword within the body of an activity. This is a tokenized match, meaning that your keyword string will be matched against the tokenized text of the activity body — tokenization is based on punctuation, symbol, and separator Unicode basic plane characters. For example, an activity with the text “I like coca-cola” would be split into the following tokens: I, like, coca, cola. These tokens would then be compared to the keyword string used in your rule. To match strings containing punctuation (e.g. coca-cola), symbol, or separator characters, you must use a quoted exact match as described below.

Gnip Rule Match No Match
gnip body: I need to call gnip.
body: Check out gnip’s documentation.
body: I like #gnip.
body: Check out Gnip.
body: #gniprocks
cola body: Ice cold cola on a hot day
body: I like coca-cola!
body: I like cocacola!
tesla body: Wanted to share- http://t.co/yECAbi9p6Q
twitter_entities.urls.url: http://t.co/yECAbi9p6Q
twitter_entities.urls.expanded_url: http://wrd.cm/1IfohKo
gnip.urls.display_url: wrd.cm/1IfohKo
gnip.urls.expanded_url: http://www.wired.com/2015/05/used-teslas/
body: Teslas for sale!
Coachella Hanging out at #coachella NEW.PICS.FROM.COACHELLA2015!

See Examples

Sources: Twitter

emoji Matches an emoji within the body of an activity. This is a tokenized match, meaning that your emoji will be matched against the tokenized text of the activity body – tokenization is based on punctuation, symbol/emoji, and separator Unicode basic plane characters. For example, an activity with the text “I like 🍕” would be split into the following tokens: I, like, 🍕. These tokens would then be compared to the emoji used in your rule.

Gnip Rule Match No Match
🍕 I need 🍕

I really like 🍕🍕

Cake pizza coffee 🍰🍕☕

I need ☕

I really like ☕☕

Pizza coffee cake 🍕☕🍰

🍕

🍕

Why do I like ☕ with 🍕 This ☕️ has a variant️ 🍕
“☕” Why do I like ☕ with 🍕 This ☕️ has a variant️ 🍕/td>

“exact phrase match” Matches an exact phrase within the body of an activity. This is an exact match, and it is not necessary to escape characters with a backslash. For example, if matching something with a slash, use “one/two”, not “one\/two”.

Note that this is not a substring match, and includes a check for word boundaries at the ends of the quoted phrase. For a pure substring match, see the contains: operator below.

Gnip Rule Match No Match
“call gnip” I need to call gnip, again
I need to call gnip again
call gnip
I called gnip
call gnip (multiple spaces)
call-gnip
call_gnip
“one/two” Maybe we can look at one/two different computers
One/two/three – fourth time’s is a charm
call gnip
#one/two hashtags with punctuation don’t work well
one//two slash happy
one\two

See Examples

Sources: Twitter

“keyword1 keyword2″~N Commonly referred to as a proximity operator, this matches an activity where the keywords are no more than N tokens from each other.

If the keywords are in the opposite order, they can not be more than N-2 tokens from each other.

Can have any number of keywords in quotes.

N cannot be greater than 6.

Gnip Rule Match No Match
“love boulder”~4 Love everything about my town Boulder.
Boulder, I love living here.
I don’t love hiking, but I really like to visit Boulder.
Boulder is a place I love to visit.

See Examples

Sources: Twitter

contains: Substring match for activities that have the given substring in the body, regardless of tokenization. In other words, this does a pure substring match, and does not consider word boundaries.

Use double quotes to match substrings that contain whitespace or punctuation.

Gnip Rule Match No Match
contains:phone Where is my phone?
That’s a telephone
Pongo la telephono.
What is the ph0ne number?
contains:”$TWTR” How much is $TWTR stock?
How much is $TWTRstock?
Headlines with $GOOG$TWTR$FB today
Just setting up my TWTR
Just setting up my $ TWTR

See Examples

Sources: Twitter

from: Matches any activity from a specific user.

In Twitter, the value must be the user’s Twitter ID or username (excluding the @ character). See [HERE](https://dev.twitter.com/rest/reference/get/users/lookup) or [HERE](http://gettwitterid.com/)

For some publishers, MD5-hashed email can be used.

Gnip Rule Match No Match
from:17200003 All original tweets from user 1720003
Retweets of others’ tweets by user 1720003
Replies made by user 1720003 on others’ tweets
Tweets from this user 1720003, regardless of user’s changed username
Retweets of user 1720003 tweets by other users
from:mikesmith All original tweets from user mikesmith
Retweets of others’ tweets by mikesmith
Retweets of mikesmith tweets by other users
Tweets from this user, with a different or changed username

See Examples

Sources: Twitter

to: Matches any activity that is in reply to a particular user.

In Twitter, the value must be the user’s numeric ID or username (excluding the @ character).

In Disqus, this operator only supports the hashed actor IDs delivered via the Disqus API, not pretty usernames or displayNames present on the platform.

Gnip Rule Match No Match
Twitter
to:gnip
to:16958875
Tweets that start as @gnip
Reply to a tweet sent originally by @gnip (Twitter ID = 16958875)
Tweet that mentions @gnip but not start with @gnip
Quote tweets of tweets from @gnip
Disqus
to:1on6

See Examples

Sources: Twitter

url: Matches activities with URLs that literally contain the given phrase or keyword. URL encodings are not encoded at this time. To search for patterns with punctuation in them (i.e. google.com) enclose the search term in quotes.

NOTE: If you’re using Gnip’s Enriched output format, we will match against Gnip’s expanded URL as well.

Gnip Rule Match No Match
url:gnip http://support.gnip.com/
https://github.com/abh1nav/gnippy
https://gn.ip.com
url:”how-to” https://www.coachella.com/how-to-purchase/
url:teslas twitter_entities.urls.url: http://t.co/yECAbi9p6Q
twitter_entities.urls.expanded_url: http://wrd.cm/1IfohKo
gnip.urls.display_url: wrd.cm/1IfohKo
gnip.urls.expanded_url: http://www.wired.com/2015/05/used-teslas/ (matches fully unwound URL)

See Examples

Sources: Twitter

has:links This operators matches activities which contain links in the message body.

Gnip Rule Match No Match
cat has:links Here’s a picture of my cat: bit.ly/cat
Adopt a cat at http://spca.org/cats
Check out @gnip
Check out #gnip

See Examples

Sources: Twitter

lang: Matches tweets that have been classified by Twitter as being of a particular language (if, and only if, the tweet has been classified). It is important to note that each activity is currently only classified as being of one language, so AND’ing together multiple languages will yield no results.

Note: if no language classification can be made the provided result is ‘und’ (for undefined).

The list below represents the current supported languages and their corresponding BCP 47 language indentifier:

  • Amharic – am
  • Arabic – ar
  • Armenian – hy
  • Bengali – bn
  • Bulgarian – bg
  • Burmese – my
  • Chinese – zh
  • Czech – cs
  • Danish – da
  • Dutch – nl
  • English – en
  • Estonian – et
  • Finnish – fi
  • French – fr
  • Georgian – ka
  • German – de
  • Greek – el
  • Gujarati – gu
  • Haitian – ht
  • Hebrew – iw
  • Hindi – hi
  • Hungarian – hu
  • Icelandic – is
  • Indonesian – in
  • Italian – it
  • Japanese – ja
  • Kannada – kn
  • Khmer – km
  • Korean – ko
  • Lao – lo
  • Latvian – lv
  • Lithuanian – lt
  • Malayalam – ml
  • Maldivian – dv
  • Marathi – mr
  • Nepali – ne
  • Norwegian – no
  • Oriya – or
  • Panjabi – pa
  • Pashto – ps
  • Persian – fa
  • Polish – pl
  • Portuguese – pt
  • Romanian – ro
  • Russian – ru
  • Serbian – sr
  • Sindhi – sd
  • Sinhala – si
  • Slovak – sk
  • Slovenian – sl
  • Sorani Kurdish – ckb
  • Spanish – es
  • Swedish – sv
  • Tagalog – tl
  • Tamil – ta
  • Telugu – te
  • Thai – th
  • Tibetan – bo
  • Turkish – tr
  • Ukrainian – uk
  • Urdu – ur
  • Uyghur – ug
  • Vietnamese – vi
  • Welsh – cy

 

Gnip Rule Match No Match
lang:fr “C’est un plaisir de vous rencontrer!” “Nice to meet you!”

See Examples

Sources: Twitter

-lang:und Matches activities which Gnip has classified as any language.

Gnip Rule Match No Match
-lang:und gnip.language.value: es gnip.language.value: null
lang:es (but gnip.language.value: null)

See Examples

Sources: Twitter

sample: Returns a random sample of activities that match a rule rather than the entire set of activities. Sample percent must be represented by an integer value between 1 and 100. This operator applies to the entire rule and requires any “OR’d” terms be grouped.

Gnip Rule Match No Match
dog sample:50 50% of tweets that match the keyword dog
(dog OR cat) sample:25 25% of tweets that match the keyword cat or the keyword dog
sample:2 2% of all tweets (Note:This is a stand alone rule for 1-10% sample)

See Examples

Sources: Twitter

# Matches any activity with the given hashtag.

This operator performs an exact match, NOT a tokenized match, meaning the rule “2016” will match posts with the exact hashtag “2016”, but not those with the hashtag “2016election”

For Twitter, note that the hashtag operator relies on Twitter’s entity extraction to match hashtags, rather than extracting the hashtag from the body itself. The description of how Twitter extracts entities can be found here: http://dev.twitter.com/pages/tweet_entities.

Gnip Rule Match No Match
#politics All posts tagged with “#politics”
#2016_election All posts tagged with “#2016_election” Posts tagged with “#2016”
#boulderfire All posts tagged with “#boulderfire” Posts tagged with “#boulderfirefighters”

See Examples

Sources: Twitter

point_radius:[lon lat radius] Matches against the Exact Location (x,y) of the Activity when present, and in Twitter, against a “Place” geo polygon, where the Place is fully contained within the defined region.

– Units of radius supported are miles (mi) and kilometers (km).
– Radius must be less than 25mi.
– Longitude is in the range of ±180
– Latitude is in the range of ±90
– All coordinates are in decimal degrees.
– Rule arguments are contained with brackets, space delimited.

Gnip Rule Match No Match
point_radius:[-105.27346517 40.01924738 0.5mi] Tweets (with place) or Foursquare Checkins within .5 miles of 17th and Pearl Street in Boulder, CO. Tweets (with place) or Foursquare Checkins outside more than .5 miles from 17th and Pearl in Boulder, CO.
Tweets without place defined
point_radius:[2.355128 48.861118 16km] Tweets (with place) or Foursquare Checkins within 16 kilometers of the center of Paris, France Tweets (with place) or Foursquare Checkins outside more than 16 kilometers from the center of Paris, France
Tweets without place defined

See Examples

Sources: Twitter

bounding_box:[west_long south_lat east_long north_lat] Matches against the Exact Location (x,y) of the Activity when present, and in Twitter, against a “Place” geo polygon, where the Place is fully contained within the defined region.

– west_long south_lat represent the southwest corner of the bounding box where west-long is the longitude of that point, and south_lat is the latitude.
– east_long and north_lat represent the northeast corner of the bounding box, where east_long is the longitude of that point, and north_lat is the latitude.
– Width and height of the bounding box must be less than 25mi
– Longitude is in the range of ±180
– Latitude is in the range of ±90
– All coordinates are in decimal degrees.
– Rule arguments are contained with brackets, space delimited.

Gnip Rule Match No Match
bounding_box:[-105.301758 39.964069 -105.178505 40.09455] Tweets (with place) or Foursquare Checkins with coordinates contained within a box drawn around Boulder, CO Tweets (with place) or Foursquare Checkins outside the box drawn around Boulder, CO
Tweets without place defined.

See Examples

Sources: Twitter

@ Matches any tweet that mentions the given username. Note that this does not support user IDs as an argument. The username should not be quoted.

Note that the mention operator relies on Twitter’s entity extraction to match mentions, rather than trying to extract the mention from the body itself. The description of how Twitter extracts entities can be found here: [http://dev.twitter.com/pages/tweet_entities](http://dev.twitter.com/pages/tweet_entities).

Gnip Rule Match No Match
@gnip cool @gnip stuff cool stuff @gnipeng
cool stuff #gnip
@kaskade .@kaskade
twitter_entities.user_mentions.screen_name:kaskade
@ Coachella

See Examples

Sources: Twitter

bio: Matches a keyword within the user bio of a Tweet. This is a tokenized match within the contents of a user’s “bio” field.

Gnip Rule Match No Match
bio:snow Loves snow vacations.
Into snow sports.
If snowing- find me on the mountain

See Examples

bio_name: Matches a keyword within the user bio name of a Tweet. This is a tokenized match within the contents of a user’s “Name” field.

Gnip Rule Match No Match
bio_name:smith John Smith
Smith works
JaneSmith

See Examples

retweets_of: Matches tweets that are retweets of a specified user. Accepts both usernames and numeric Twitter IDs (NOT tweet status IDs)

Gnip Rule Match No Match
retweets_of:justinbieber When verb:share this matches on the object.actor.preferredUsername:justinbieber
Retweets of organic tweets from justinbieber account
Retweets of retweets by justinbieber
Quoted justinbieber tweets
retweets_of:6264412

See Examples

Sources: Twitter

bio_contains: Matches tweets whose author’s Twitter bio contain the given substring. To search for patterns with punctuation in them (i.e. start-up) enclose the search term in quotes.

Gnip Rule Match No Match
bio_contains:CEO “CEO of ABC Corp” “COO at DEF, Inc.”
bio_contains:”Start-up” “Start-up junkie” “Software Engineer startup @Gnip”
bio_contains:”bieber” “World’s biggest @justinbieber fan” “I love biebs”

See Examples

Sources: Twitter

lang: Matches tweets that have been classified by Twitter as being of a particular language (if, and only if, the tweet has been classified). It is important to note that each activity is currently only classified as being of one language, so AND’ing together multiple languages will yield no results. The list below represents the current supported languages and their corresponding BCP 47 language indentifier:

  • Amharic – am
  • Arabic – ar
  • Armenian – hy
  • Bengali – bn
  • Bosnian – bs
  • Bulgarian – bg
  • Cherokee – chr
  • Chinese – zh
  • Croatian – hr
  • Danish – da
  • Dutch – nl
  • English – en
  • Estonian – et
  • Finnish – fi
  • French – fr
  • Georgian – ka
  • German – de
  • Greek – el
  • Gujarati – gu
  • Haitian – ht
  • Hebrew – iw
  • Hindi – hi
  • Hungarian – hu
  • Icelandic – is
  • Indonesian – in
  • Inuktitut – iu
  • Italian – it
  • Japanese – ja
  • Kannada – kn
  • Khmer – km
  • Korean – ko
  • Lao – lo
  • Latvian – lv
  • Lithuanian – lt
  • Malayalam – ml
  • Maldivian – dv
  • Marathi – mr
  • Myanmar-Burmese – my
  • Nepali – ne
  • Norwegian – no
  • Oriya – or
  • Panjabi – pa
  • Pashto – ps
  • Persian – fa
  • Polish – pl
  • Portuguese – pt
  • Romanian – ro
  • Russian – ru
  • Serbian – sr
  • Sindhi – sd
  • Sinhala – si
  • Slovak – sk
  • Slovenian – sl
  • Sorani Kurdish – ckb
  • Spanish – es
  • Swedish – sv
  • Tagalog – tl
  • Tamil – ta
  • Telugu – te
  • Thai – th
  • Tibetan – bo
  • Turkish – tr
  • Ukrainian – uk
  • Urdu – ur
  • Uyghur – ug
  • Vietnamese – vi
  • Welsh – cy

 

 

Gnip Rule Match No Match
lang:fr “C’est un plaisir de vous rencontrer!” “Nice to meet you!”

See Examples

Sources: Twitter

bio_name_contains: Matches tweets where the user’s display name (not username) as specified in their bio, contains a given substring.

Gnip Rule Match No Match
bio_name_contains:”Mike” (Any tweets from a user whose said they were named Mike in their Twitter bio)

See Examples

Sources: Twitter

bio_location: Matches tweets where the user’s bio-level location contains the specified keyword or phrase. This operator performs a tokenized match, similar to the normal keyword rules on the message body.

The user bio location is a non-normalized, user-generated, free-form string.

Gnip Rule Match No Match
bio_location:”boulder” actor.location.displayname:Boulder
actor.location.displayname:Boulder, CO
actor.location.displayname:Boulder Colorado
actor.location.displayname:Beautiful Boulder, CO
actor.location.displayname:BoCo
actor.location.displayname:Boulderado
actor.location.displayname:Colorado

See Examples

Sources: Twitter

bio_location_contains: Matches Tweets where the user’s bio-level location contains the specified substring.

The user bio location is a non-normalized, user-generated, free-form string.

**Warning**: use of broad or common locations strings can result in the consumption of large volumes of data (e.g. a bio_location_contains:”MA” rule with hopes of matching all tweets from Massachusetts, will also match “Alabama”). The addition of punctuation (e.g. “, MA” or “,MA”) could help limit this data.

Gnip Rule Match No Match
bio_location_contains:”AZ” actor.location.displayname:Pheonix, AZ
actor.location.displayname:Beautiful Pheonix, AZ
actor.location.displayname:Aztec Ruins
actor.location.displayname:Arizona
actor.location.displayname:USA
bio_location_contains:”, MA” actor.location.displayname:Boston, MA
actor.location.displayname:Andapa, Madagascar
actor.location.displayname:Alabama
actor.location.displayname:Mass

See Examples

Sources: Twitter

time_zone: Matches tweets where the user-selected time zone specified in a user’s profile settings matches a given string.

These values are normalized to the options specified on a user’s account settings page: [https://twitter.com/account/settings]

Gnip Rule Match No Match
time_zone:”Eastern Time (US & Canada)” Tweets from accounts that have their account time zone set to “(GMT -04:00) Eastern Time (US & Canada)” at the time of the tweet Tweets from accounts that do not have their account time zone set to “Eastern Time (US & Canada)”
time_zone:”Dublin” Tweets from accounts that have their account time zone set to “(GMT+01:00) Dublin” at the time of the tweet Tweets from accounts that do not have their account time zone set to “(GMT+01:00) West Central Africa” Note:Timezones are specific, not grouped by UTC offset.

See Examples

statuses_count: Matches tweets where the author has posted a number of statuses that falls within the given range.

If a single number is specified, any number equal to or higher will match.

Additionally, a range can be specified to match any number in the given range.

Gnip Rule Match No Match
statuses_count:1000 Tweets (from user) that have statuses_count:1000 or more Tweets (from user) that have statuses_count:999 or less
statuses_count:1000..10000 Tweets (from user) that have statuses_count:1000
Tweets (from user) that have statuses_count:6814
Tweets (from user) that have statuses_count:10000
Tweets (from user) that have statuses_count:999 or less
Tweets (from user) that have statuses_count:10001 or more

See Examples

Sources: Twitter

followers_count: Matches tweets where the author has a followers count within the given range.

If a single number is specified, any number equal to or higher will match.

Additionally, a range can be specified to match any number in the given range.

Gnip Rule Match No Match
followers_count:1000 Tweets (from user) that have followers_count:1000 or more Tweets (from user) that have followers_count:999 or less
followers_count:1000..10000 Tweets (from user) that have followers_count:1000
Tweets (from user) that have followers_count:6814
Tweets (from user) that have followers_count:10000
Tweets (from user) that have followers_count:999 or less
Tweets (from user) that have followers_count:10001 or more

See Examples

Sources: Twitter

friends_count: Matches tweets where the author has a friends count (the number of users they follow) that falls within the given range.

If a single number is specified, any number equal to or higher will match.

Additionally, a range can be specified to match any number in the given range.

Gnip Rule Match No Match
friends_count:1000 Tweets (from user) that have friends_count:1000 or more Tweets (from user) that have friends_count:999 or less
friends_count:1000..10000 Tweets (from user) that have friends_count:1000
Tweets (from user) that have friends_count:6814
Tweets (from user) that have friends_count:10000
Tweets (from user) that have friends_count:999 or less
Tweets (from user) that have friends_count:10001 or more

See Examples

Sources: Twitter

listed_count: Matches tweets where the author has been listed within Twitter a number of times falls within the given range.

If a single number is specified, any number equal to or higher will match.

Additionally, a range can be specified to match any number in the given range.

Gnip Rule Match No Match
listed_count:1000 Tweets (from user) that have listed_count:1000 or more Tweets (from user) that have listed_count:999 or less
listed_count:1000..10000 Tweets (from user) that have listed_count:1000
Tweets (from user) that have listed_count:6814
Tweets (from user) that have listed_count:10000
Tweets (from user) that have listed_count:999 or less
Tweets (from user) that have listed_count:10001 or more

See Examples

Sources: Twitter

is:verified Deliver only Tweets where the author is “verified” by Twitter. Can also be negated to exclude Tweets where the author is verified.

Gnip Rule Match No Match
dog is:verified Tweets from verified users with the keyword dog
cat -is:verified Tweets only from not verified users with the keyword dog
dog OR (cat is:verified) Tweets containing the keyword dog or Tweets from verified users with the keyword cat
(dog OR cat) is:verified Tweets from verified users with either the keyword dog or the keyword cat

See Examples

Sources: Twitter

source: Matches any tweet generated by the given source application. The value must be either the name of the application, or the application’s URL. Cannot be used alone.

Gnip Rule Match No Match
cat source:web cool cat (if the tweet was created at twitter.com) cool cat (if the tweet was created from an iPhone)
cat -source:web neat cat (if the tweet was NOT from twitter.com) neat cat (if the tweet was created at twitter.com)
cat source:”Twitter for iPhone” neat cat (if the tweet was from an iPhone Twitter App) neat cat (if the tweet was created from an Android)
cat source:iphone neat cat (if the tweet was from an iPhone Twitter App)
generator.displayName:Twitter for iPhone
cat source:”Android” neat cat (if the tweet was from an Android Twitter App)
generator.displayName:Twitter for Android
cat source:tweetdeck neat cat (if the tweet was by TweetDeck)
generator.link:https://about.twitter.com/products/tweetdeck
cat source:Emily neat cat (if the tweet was created by the EmilyTestPublicAPI App)
generator.displayName:EmilyTestPublicAPI

See Examples

place: Matches tweets tagged with the specified location. Multi-word place names (“New York City”, “Palo Alto”) should be enclosed in quotes.

Gnip Rule Match No Match
place:”Rio de Janeiro” Tweets that are geo-tagged with the exact place.name Rio de Janeiro Tweets where place:null
place:Florida Tweets that are geo-tagged with the exact place.name:Florida Tweets that are geo-tagged with place.name:USA
Tweets where place:null
place:fd70c22040963ac7 Tweets that are geo-tagged with the exact Twitter place.id:fd70c22040963ac7
Tweets that are geo-tagged with Boulder, CO (place.id:fd70c22040963ac7)
Tweets where place.id:e21c8e4914eef2b3 (Note: this is the placeID for the state Colorado)
Tweets where place:null

See Examples

Sources: Twitter

place_country: Matches tweets where the country code associated with a tagged [place/location](https://dev.twitter.com/overview/api/places) matches the given ISO alpha-2 character code.

Valid ISO codes can be found here: [http://en.wikipedia.org/wiki/ISO_3166-1_alpha-2](http://en.wikipedia.org/wiki/ISO_3166-1_alpha-2)

Gnip Rule Match No Match
place_country:us Tweets with the United States place/location country code
location.twitter_place_country:US
place_country:GB Tweets with the Great Britain place/location country code
location.twitter_place_country:GB
place_country:UK No matches, UK is not an ISO alpha-2 country code
place_countrye:USA No matches, USA is not an ISO alpha-2 country code

See Examples

Sources: Twitter

has:geo Matches tweets that have Tweet-specific geo location data provided from Twitter. This can be either “geo” lat-long coordinate, or a “location” in the form of a Twitter [“Place”](https://dev.twitter.com/overview/api/places), with corresponding display name, geo polygon, and other fields.

WARNING: Use this operator with care, it can generate large amounts of volume. Currently, this will deliver 1-4% of the firehose independently.

Gnip Rule Match No Match
sale has:geo Any Tweets with geolocation data, either an exact lat/lon or a named “place”, that also have the keyword ‘sale’ in the body of the Tweet Tweets that have keyword ‘sale’ but do not have a place/location
sale -has:geo Tweets that have keyword ‘sale’ that do not have a place/location

See Examples

Sources: Twitter

has:mentions Matches tweets that mention another Twitter user.

WARNING: Use this operator with care. Used by itself, with no other limiting clauses, it can generate large amounts of volume. Currently, this will deliver double digit percentages of the firehose when used by itself.

Gnip Rule Match No Match
“best friends” has:mentions Tweets that mention other users and have the phrase “best friends”
enemies -has:mentions Tweets that have the keyword enemies and do not mention other users

See Examples

Sources: Twitter

has:hashtags Matches tweets that contain a hashtag.

WARNING: Use this operator with care. Used by itself, with no other limiting clauses, it can generate large amounts of volume. Currently, this will deliver double digit percentages of the firehose when used by itself.

Gnip Rule Match No Match
cat has:hashtags My cat is too fat. #diet
My cat just had kittens. #cute

See Examples

Sources: Twitter

has:media Matches tweets that contain a media url classified by Twitter, e.g. pic.twitter.com.

WARNING: Use this operator with care. Used by itself, with no other limiting clauses, it can generate large amounts of volume. Currently, this will deliver double digit percentages of the firehose when used by itself.

Gnip Rule Match No Match
has:media (Any tweets that contain a media url as classified by Twitter including images and videos)

See Examples

Sources: Twitter

is:retweet Deliver only explicit retweets that match a rule. Can also be negated to exclude retweets that match a rule from delivery and only original content is delivered.

NOTE: This operator looks only for true Retweets, which use Twitter’s retweet functionality. Quoted Tweets and Modified Tweets which do not use Twitter’s retweet functionality will not be matched by this operator.

Gnip Rule Match No Match
dog is:retweet
cat -is:retweet
dog OR (cat is:retweet)
(dog OR cat) is:retweet

See Examples

Sources: Twitter

has:profile_geo Matches tweets that have any [Profile Geo](http://support.gnip.com/enrichments/profile_geo.html) metadata, regardless of the actual value.

Gnip Rule Match
cat has:profile_geo If account is enabled for the Profile-Geo Enrichment, this will match any Tweets that mentions the word “cat” and has any Gnip Profile Geo metadata derived from the user’s bio “location”. Tweets from accounts that do not have a bio “location” entered by the user

See Examples

Sources: Twitter

profile_country: Exact match on the “countryCode” field from the “address” object in the Profile Geo enrichment.

Uses a normalized set of two-letter country codes, based on ISO-3166-1-alpha-2 specification. This operator is provided in lieu of an operator for “country” field from the “address” object to be concise.

Gnip Rule Match
profile_country:us All Profile Geo Enrichments in the United States.

See Examples

Sources: Twitter

profile_region_contains: Matches on the “region” field from the “address” object in the Profile Geo enrichment.

This is a substring match for activities that have the given substring in the body, regardless of tokenization. Use double quotes to match substrings that contain whitespace or punctuation.

Gnip Rule Match
profile_region_contains:carolina All Profile Geo Enrichments in North or South Carolina (or other regions of the world containing the string “Carolina”)

See Examples

Sources: Twitter

profile_locality_contains: Matches on the “locality” field from the “address” object in the Profile Geo enrichment.

This is a substring match for activities that have the given substring in the body, regardless of tokenization. Use double quotes to match substrings that contain whitespace or punctuation.

Gnip Rule Match
profile_locality_contains:haven All Profile Geo Enrichments in ANY city containing the substring “haven” including “New Haven,” “West Haven,” and “Lock Haven”

See Examples

Sources: Twitter

profile_subregion_contains: Matches on the “subRegion” field from the “address” object in the Profile Geo enrichment. In addition to targeting specific counties, these operators can be helpful to filter on a metro area without defining filters for every city and town within the region.

This is a substring match for activities that have the given substring in the body, regardless of tokenization. Use double quotes to match substrings that contain whitespace or punctuation.

Gnip Rule Match
profile_subregion_contains:jefferson All Profile Geo Enrichments where the substring ‘jefferson’ appears in the subRegion (e.g. ‘Jefferson County’)

See Examples

Sources: Twitter

profile_subregion: Matches on the “subRegion” field from the “address” object in the Profile Geo enrichment. In addition to targeting specific counties, these operators can be helpful to filter on a metro area without defining filters for every city and town within the region.

This is an exact full string match. It is not necessary to escape characters with a backslash. For example, if matching something with a slash, use “one/two”, not “one\/two”. Use double quotes to match substrings that contain whitespace or punctuation.

Gnip Rule Match
profile_subregion:”San Francisco County” All Profile Geo Enrichments where the subRegion is San Francisco County.
profile_subregion:”San Mateo County” All Profile Geo Enrichments where the subRegion is San Mateo County.

See Examples

has:images Query for only tweets with native images.
has:videos Query for only tweets with native videos.
has:symbols Symbols are also known as cashtags, has:symbols supports querying for only tweets that have symbols/cashtags
$ Query for tweets with a given symbol/cashtag (e.g., $AAPL).

Restrictions

  1. Stop words are not allowed as stand-alone terms in queries. If you need to find a phrase that contains a stop word, either pair it with an additional term, or use the exact match operators such as “on the roof”. As long as there is at least one required and allowed term in the rule, it will be allowed. Please note that this list of stop words is subject to change, but the current stop words we use are: “a”, “an”, “and”, “at”, “but”, “by”, “com”, “from”, “http”, “https”, “if”, “in”, “is”, “it”, “its”, “me”, “my”, “or”, “rt”, “the”, “this”, “to”, “too”, “via”, “we”, “www”, “you”
  2. Rules cannot consist of only negated terms/operators. For example, ‘-cat -dog’ is not valid.
  3. Negated ORs are not supported. Such as: apple OR -lang:en
  4. Each rule may only have 1 tag. However, the tag is simply treated as a string, and may contain up to 255 characters, including – ; and other punctuation.
  5. Geo rules with a radius greater than 25mi are not supported. Geo rules with a bounding box comprised of any edge greater than 25mi are not supported.
  6. A rule operator can start with either a digit (0-9) or any non-punctuation character. Current punctuation characters are defined as the ASCII characters below. Any operator that needs to start with these characters must be quoted. Generally, punctuation can be embedded within the term. Punctuation characters used to construct rules (colon and parentheses), however, are not allowed and thus a term requiring it to be embedded must be quoted.
! % & \ ' ( ) * + - . / ; < = > ? \\ , : # @ \t \r \n " [] _
and the Unicode ranges:
U+007B -- U+00BF
U+02B0 -- U+037F
U+2000 -- U+2BFF
U+FF00 -- U+FF03
U+FF05 -- U+FF0F