Twitter Trends United states Scraper

Scrape trending tweets and hashtags on Twitter from any US states locations 🌍.

This actor scrapes the top US states locations Live trends about trending topics, tweets, hashtags on Twitter from any of the states locations listed below. It utilizes proxies to ensure reliable scraping and to avoid rate limits imposed by the endpoint. The actor outputs the trends for each country as an array of JSON objects, including the time of scraping, trend name, the time it was trending, and its volume of tweets. This data can be useful for:

• Market research 📊: Analyze trending topics to understand consumer interests and preferences in different regions.
• Social media monitoring 📢: Track trending topics to identify emerging news and conversations.
• Content creation ✍️: Use trending topics as inspiration for content creation relevant to specific audiences.
• Competitive analysis 📈: Monitor competitor activity and industry trends.

Supported Countries:

The actor outputs a JSON array containing the Live trends for the following locations in United States:
Time periods are:
** Live **

for:

• New York
• Los Angeles
• Chicago
• Albuquerque
• Atlanta
• Austin
• Baltimore
• Baton Rouge
• Birmingham
• Boston
• Charlotte
• Cincinnati
• Cleveland
• Colorado Springs
• Columbus
• Dallas-Ft. Worth
• Denver
• Detroit
• El Paso
• Fresno
• Greensboro
• Harrisburg
• Honolulu
• Houston
• Indianapolis
• Jackson
• Jacksonville
• Kansas City
• Las Vegas
• Long Beach
• Louisville
• Memphis
• Mesa
• Miami
• Milwaukee
• Minneapolis
• Nashville
• New Haven
• New Orleans
• Norfolk
• Oklahoma City
• Omaha
• Orlando
• Philadelphia
• Phoenix
• Pittsburgh
• Portland
• Providence
• Raleigh
• Richmond
• Sacramento
• St. Louis
• Salt Lake City
• San Antonio
• San Diego
• San Francisco
• San Jose
• Seattle
• Tallahassee
• Tampa
• Tucson
• Virginia Beach
• Washington*

The United States country level and Worldwide selection outputs a JSON array containing objects, where each object represents a trending topic at a specific point in time. Here's an example:

Time periods are: ** Live **
** 1 hour ago **
** 3 hours ago **
** 6 hours ago **
** 12 hour ago **
** 24 hours ago **
** 2 days ago **
** 3 days ago **

Output Example for New York Trends Live:

1[
2{
3"time": "2024-11-11T11:37:33.195Z",
4"timePeriod": "Live",
5"trend": "No Labels",
6"volume": "43,244Tweets"
7},
8{
9"time": "2024-11-11T11:37:33.195Z",
10"timePeriod": "Live",
11"trend": "Jack Smith",
12"volume": "142,670Tweets"
13},
14{
15"time": "2024-11-11T11:37:33.195Z",
16"timePeriod": "Live",
17"trend": "#SkylosFairLaunch",
18"volume": ""
19},
20{
21"time": "2024-11-11T11:37:33.195Z",
22"timePeriod": "Live",
23"trend": "#TrumpIndictment",
24"volume": "18,473Tweets"
25},
26{
27"time": "2024-11-11T11:37:33.195Z",
28"timePeriod": "Live",
29"trend": "#ProtectMyFreedomToVote",
30"volume": ""
31},
32{
33"time": "2024-11-11T11:37:33.195Z",
34"timePeriod": "Live",
35"trend": "Subway",
36"volume": "28,523Tweets"
37},
38{
39"time": "2024-11-11T11:37:33.195Z",
40"timePeriod": "Live",
41"trend": "Orwell",
42"volume": ""
43},
44{
45"time": "2024-11-11T11:37:33.195Z",
46"timePeriod": "Live",
47"trend": "McCarthy",
48"volume": "40,872Tweets"
49},
50{
51"time": "2024-11-11T11:37:33.195Z",
52"timePeriod": "Live",
53"trend": "Freedom to Vote Act",
54"volume": ""
55},
56{
57"time": "2024-11-11T11:37:33.195Z",
58"timePeriod": "Live",
59"trend": "Denji",
60"volume": "18,133Tweets"
61},
62{
63"time": "2024-11-11T11:37:33.195Z",
64"timePeriod": "Live",
65"trend": "North Korea",
66"volume": "34,628Tweets"
67},
68{
69"time": "2024-11-11T11:37:33.195Z",
70"timePeriod": "Live",
71"trend": "Llama 2",
72"volume": ""
73},
74{
75"time": "2024-11-11T11:37:33.195Z",
76"timePeriod": "Live",
77"trend": "Marge",
78"volume": "10,694Tweets"
79},
80{
81"time": "2024-11-11T11:37:33.195Z",
82"timePeriod": "Live",
83"trend": "Taco Tuesday",
84"volume": "16,120Tweets"
85},
86{
87"time": "2024-11-11T11:37:33.195Z",
88"timePeriod": "Live",
89"trend": "#tuesdayvibe",
90"volume": "24,763Tweets"
91},
92{
93"time": "2024-11-11T11:37:33.195Z",
94"timePeriod": "Live",
95"trend": "DO SOMETHING",
96"volume": "153,596Tweets"
97},
98{
99"time": "2024-11-11T11:37:33.195Z",
100"timePeriod": "Live",
101"trend": "The DOJ",
102"volume": "86,746Tweets"
103},
104{
105"time": "2024-11-11T11:37:33.195Z",
106"timePeriod": "Live",
107"trend": "$MSFT",
108"volume": ""
109},
110{
111"time": "2024-11-11T11:37:33.195Z",
112"timePeriod": "Live",
113"trend": "Hugh Freeze",
114"volume": ""
115},
116{
117"time": "2024-11-11T11:37:33.195Z",
118"timePeriod": "Live",
119"trend": "Burrow",
120"volume": ""
121},
122{
123"time": "2024-11-11T11:37:33.195Z",
124"timePeriod": "Live",
125"trend": "Browns",
126"volume": "22,633Tweets"
127},
128{
129"time": "2024-11-11T11:37:33.195Z",
130"timePeriod": "Live",
131"trend": "Bayern",
132"volume": "87,080Tweets"
133},
134{
135"time": "2024-11-11T11:37:33.195Z",
136"timePeriod": "Live",
137"trend": "iphone wifi",
138"volume": ""
139},
140{
141"time": "2024-11-11T11:37:33.195Z",
142"timePeriod": "Live",
143"trend": "Odor",
144"volume": ""
145},
146{
147"time": "2024-11-11T11:37:33.195Z",
148"timePeriod": "Live",
149"trend": "Mark Meadows",
150"volume": ""
151},
152{
153"time": "2024-11-11T11:37:33.195Z",
154"timePeriod": "Live",
155"trend": "Rita Wilson",
156"volume": ""
157},
158{
159"time": "2024-11-11T11:37:33.195Z",
160"timePeriod": "Live",
161"trend": "Herzog",
162"volume": "13,205Tweets"
163},
164{
165"time": "2024-11-11T11:37:33.195Z",
166"timePeriod": "Live",
167"trend": "RIP Harvey",
168"volume": ""
169},
170{
171"time": "2024-11-11T11:37:33.195Z",
172"timePeriod": "Live",
173"trend": "#EN_WORLDTOUR_FATE",
174"volume": "10,201Tweets"
175},
176{
177"time": "2024-11-11T11:37:33.195Z",
178"timePeriod": "Live",
179"trend": "Garland",
180"volume": "40,972Tweets"
181},
182{
183"time": "2024-11-11T11:37:33.195Z",
184"timePeriod": "Live",
185"trend": "Sorc",
186"volume": ""
187},
188{
189"time": "2024-11-11T11:37:33.195Z",
190"timePeriod": "Live",
191"trend": "Grand Jury",
192"volume": "78,756Tweets"
193},
194{
195"time": "2024-11-11T11:37:33.195Z",
196"timePeriod": "Live",
197"trend": "Fournier",
198"volume": ""
199},
200{
201"time": "2024-11-11T11:37:33.195Z",
202"timePeriod": "Live",
203"trend": "Tupac Shakur",
204"volume": ""
205},
206{
207"time": "2024-11-11T11:37:33.195Z",
208"timePeriod": "Live",
209"trend": "John Edwards",
210"volume": ""
211},
212{
213"time": "2024-11-11T11:37:33.195Z",
214"timePeriod": "Live",
215"trend": "Thibs",
216"volume": ""
217},
218{
219"time": "2024-11-11T11:37:33.195Z",
220"timePeriod": "Live",
221"trend": "Zach Arnett",
222"volume": ""
223},
224{
225"time": "2024-11-11T11:37:33.195Z",
226"timePeriod": "Live",
227"trend": "Kudus",
228"volume": "16,058Tweets"
229},
230{
231"time": "2024-11-11T11:37:33.195Z",
232"timePeriod": "Live",
233"trend": "Newark",
234"volume": ""
235},
236{
237"time": "2024-11-11T11:37:33.195Z",
238"timePeriod": "Live",
239"trend": "Beto",
240"volume": "16,516Tweets"
241},
242{
243"time": "2024-11-11T11:37:33.195Z",
244"timePeriod": "Live",
245"trend": "LOCK HIM UP",
246"volume": ""
247},
248{
249"time": "2024-11-11T11:37:33.195Z",
250"timePeriod": "Live",
251"trend": "Lock Her Up",
252"volume": ""
253},
254{
255"time": "2024-11-11T11:37:33.195Z",
256"timePeriod": "Live",
257"trend": "Nerlens Noel",
258"volume": ""
259},
260{
261"time": "2024-11-11T11:37:33.195Z",
262"timePeriod": "Live",
263"trend": "Target Letter",
264"volume": "82,180Tweets"
265},
266{
267"time": "2024-11-11T11:37:33.195Z",
268"timePeriod": "Live",
269"trend": "Lamar Jackson",
270"volume": ""
271},
272{
273"time": "2024-11-11T11:37:33.195Z",
274"timePeriod": "Live",
275"trend": "Fujimoto",
276"volume": ""
277},
278{
279"time": "2024-11-11T11:37:33.195Z",
280"timePeriod": "Live",
281"trend": "Jake Paul",
282"volume": ""
283},
284{
285"time": "2024-11-11T11:37:33.195Z",
286"timePeriod": "Live",
287"trend": "Mar-a-Lago",
288"volume": "27,383Tweets"
289},
290{
291"time": "2024-11-11T11:37:33.195Z",
292"timePeriod": "Live",
293"trend": "Josh Allen",
294"volume": ""
295},
296{
297"time": "2024-11-11T11:37:33.195Z",
298"timePeriod": "Live",
299"trend": "Jordan Love",
300"volume": ""
301}
302]

Understanding the timePeriod Field

The timePeriod field is crucial for understanding the context of each trend. It indicates when the specific trend was observed, relative to the time the actor was run. Here's a breakdown of possible values:

• "Live": This means the trend is currently trending in real-time.
• "X hour(s) ago": (for US country and worldwide level) This indicates the trend was trending X hours before the actor's execution.
• "X day(s) ago": (for US country and worldwide level) This indicates the trend was trending X days before the actor's execution.

By analyzing the timePeriod along with the trend itself, you can gain valuable insights into how trends evolve over time. For example, you can identify trends that are quickly gaining popularity ("Live") versus trends that have been sustained for a longer period ("X days ago").

Notes

• Twitter may occasionally change its website structure or API, which could impact the actor's functionality. If you encounter any issues, please report them on the issues tab.
• The actor relies on proxies to avoid being blocked and stay anonymous. However, there's always a small chance to be detected and block the proxies. If this happens, you might need to adjust your proxy settings to Residential, they are fast and much less blocked.