Our platform stores and analyzes X (Twitter) data to provide you with valuable insights on the latest trends and help you stay up-to-date with the audience. Discover what's currently trending, track trend history(30 days), and explore popular hashtags and topics over the long term.
Here are the current top X (Twitter) trending topics Pittsburgh, as of 44 minutes ago.
Rank | Trending Topic / Hashtag | Tweet Volume | Copy |
---|---|---|---|
1 | #HereWeGo
below 10k
|
below 10k | Copy |
2 | Tomlin
below 10k
|
below 10k | Copy |
3 | Tippett
below 10k
|
below 10k | Copy |
4 | Cavs
12.2k
|
12.2k | Copy |
5 | Minkah
below 10k
|
below 10k | Copy |
6 | Alabama
76.2k
|
76.2k | Copy |
7 | #Flyers
below 10k
|
below 10k | Copy |
8 | Henry
69.6k
|
69.6k | Copy |
9 | Pickens
below 10k
|
below 10k | Copy |
10 | Tom Brady
below 10k
|
below 10k | Copy |
11 | Chiefs
131.6k
|
131.6k | Copy |
12 | Warren
54.9k
|
54.9k | Copy |
13 | Tennessee
119.9k
|
119.9k | Copy |
14 | Ohio State
67.5k
|
67.5k | Copy |
15 | Jeremiah Smith
below 10k
|
below 10k | Copy |
16 | Michigan
48.3k
|
48.3k | Copy |
17 | Nico
72.5k
|
72.5k | Copy |
18 | Ryan Day
11.8k
|
11.8k | Copy |
19 | Howard
24.8k
|
24.8k | Copy |
20 | #GoBucks
below 10k
|
below 10k | Copy |
21 | Buckeyes
16.7k
|
16.7k | Copy |
22 | UConn
14.9k
|
14.9k | Copy |
23 | Paige
21k
|
21k | Copy |
24 | Panama
71.2k
|
71.2k | Copy |
25 | Heupel
below 10k
|
below 10k | Copy |
26 | #CFBPlayoff
38.6k
|
38.6k | Copy |
27 | Columbus
22.8k
|
22.8k | Copy |
28 | Knoxville
below 10k
|
below 10k | Copy |
29 | Jack Sawyer
below 10k
|
below 10k | Copy |
30 | Clemson
57.2k
|
57.2k | Copy |
31 | The SEC
142.3k
|
142.3k | Copy |
32 | Rocky Top
below 10k
|
below 10k | Copy |
33 | Tatum
23.1k
|
23.1k | Copy |
34 | North Crowley
below 10k
|
below 10k | Copy |
35 | #OSUvsTEN
below 10k
|
below 10k | Copy |
36 | Tim Banks
below 10k
|
below 10k | Copy |
37 | Finch
below 10k
|
below 10k | Copy |
38 | Sarah Strong
below 10k
|
below 10k | Copy |
39 | Cole Anthony
below 10k
|
below 10k | Copy |
40 | Devin Brown
below 10k
|
below 10k | Copy |
41 | Ravens
90.2k
|
90.2k | Copy |
42 | Chip Kelly
below 10k
|
below 10k | Copy |
43 | Neyland North
below 10k
|
below 10k | Copy |
44 | Jalen Brunson
below 10k
|
below 10k | Copy |
45 | Juju Watkins
below 10k
|
below 10k | Copy |
46 | Usyk
204.5k
|
204.5k | Copy |
47 | Herbstreit
below 10k
|
below 10k | Copy |
48 | Rose Bowl
below 10k
|
below 10k | Copy |
49 | Steelers
72.6k
|
72.6k | Copy |
Note: Trends refreshes every Hour . to view the Hourly trending Trends for last 24 hour click of Expand view button below |
Get real-time updates on the hottest trending hashtags and topics on X (Twitter) Pittsburgh with our automated page. Our page utilizes the official X (Twitter) API to refresh the most talked-about hashtags and topics every 30 minutes, ensuring you always have access to the most current trends on the platform.
These trending hashtags and topics are also popular on other social media platforms like Instagram, YouTube, and Facebook. Bookmark our page to stay updated on what's trending on X (Twitter) Pittsburgh .
X (Twitter) uses a combination of factors to determine the most popular and relevant trending topics on the platform. These include the number of tweets, the number of users tweeting about a topic, and the level of engagement on tweets related to the topic. The platform's algorithms also consider the user's location and language to display trends that are relevant to their specific region and language.
Trending topics on X (Twitter) are not influenced by personal opinions or paid promotions, but rather reflect the interests and conversations of the X (Twitter) community as a whole. The platform's algorithms are designed to identify topics that are popular among a wide range of users, rather than just a small group. Any attempts to artificially manipulate trending topics are strictly prohibited by Twitter's policies.
X (Twitter) 's trending algorithm is designed to identify significant increases in the popularity of a specific topic or hashtag, compared to its usual level of activity. To do this, the algorithm takes into account both the volume of tweets about the topic (the number of tweets) and the time it takes to generate that volume of tweets. Time is a critical factor in determining trends, as a topic that gradually builds in popularity over the course of a month may generate a large number of tweets after 30 days, but may not have experienced a sudden spike in activity that would qualify it as a trend. By considering both volume and time, X (Twitter) 's trending algorithm is able to identify trends that are both popular and relevant to the current conversation on the platform.