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How to find the topics any user follows in twitter with python?

Iam just trying to build a web-application and try to find the topics any user follows. I tried to get the timeline and run a topic-model(classifier) but it really takes time. Is there any way to find that from tweepy's get_user function? thnks

i would probably study twitter API
i have developed a Telegram bot, hosted as a google web app, to interface to twitter, and the API was quite rich
good luck

There's no direct way to find topics that a user follows via the Twitter API, but in Twitter API v2, you can discover the topics that are associated with the Tweets a user is posting.
(note that, at the time of posting this answer, Tweepy does not yet support API v2, but they are working on that - there are some Python samples in the TwitterDev GitHub, which use standard Python libraries where possible to do the work)
The general steps here are:
have an approved Twitter developer account, and create a new project and app for access to the v2 API
call the user Tweet timeline endpoint for the user ID you are interested in, adding the context_annotations Tweet field to your request, so that each Tweet is returned with the topic information attached.
This will return data in a format something like this (using the #TwitterDev account as an example):
{
"data": [
{
"context_annotations": [
{
"domain": {
"description": "Top level interests and hobbies groupings, like Food or Travel",
"id": "65",
"name": "Interests and Hobbies Vertical"
},
"entity": {
"description": "Technology and computing",
"id": "848920371311001600",
"name": "Technology"
}
},
{
"domain": {
"description": "A grouping of interests and hobbies entities, like Novelty Food or Destinations",
"id": "66",
"name": "Interests and Hobbies Category"
},
"entity": {
"description": "Computer programming",
"id": "848921413196984320",
"name": "Computer programming"
}
}
],
"created_at": "2021-01-05T22:45:35.000Z",
"id": "1346588685555306497",
"text": "\ud83d\udd11\ud83d\udd11 On Tuesday, January 12th, we\u2019re removing the ability to view existing consumer API keys from the developer portal. Be sure to save your API keys in a secure place before Tuesday to ensure your access to the #TwitterAPI is not disrupted. Learn more https://twittercommunity.com/t/ability-to-view-existing-consumer-api-keys-being-removed-from-your-developer-dashboard/147849"
}
...

Is there a way to obtain Facebook Insights for specific posts trough the Graph API?

Sorry to bother with maybe a stupid question, but I'm still a beginner with the Graph API. A little background to better understand my question: I need to run an analysis on a Facebook page (of which I'm not the owner but administrator, small size, ~4000 likes and ~150 posts, more or less one per day). What I intended to do was the following:
Obtain the data trough Graph API. Namely, I was most interested in retrieving the messages, number of likes and reach of every post
Import the data in R and identify the outliers (I mean, the posts whose likes and reach are not in line with the mean)
Look for correlations between those messages (since the page, for its nature, needs to talk about a wide range of topics, I want to understand which of them generate the most reactions and plan accordingly)
I've already done an analysis "by hand", but I want to test if it is possible to make the same conclusions without involving a human operator.
I've looked on the web for tutorials on how to use the graph API in python, but I've not been able to find something comprehensive. I've set up my API and obtained the permanent page token with manage_pages and read_insights permissions.
Here an idea of what I'm doing:`
def get_facebook_page_data(page_id, access_token):
website = "https://graph.facebook.com/v3.1/"
location = "%s/posts/" % page_id
fields = "?fields=message,id" + \
"reactions.type(LIKE).limit(0).summary(total_count).as(reactions_like)"
authentication = "&limit=100&access_token=%s" % (access_token)
request_url = website + location + fields + authentication
data = json.loads(request_data_from_url(request_url))
return data`
So, with this function I'm able to obtain the id, message and number of likes of all the posts stored inside data, and with another function I write everything on a csv file.
First question: am I doing something wrong?
Second question: I cannot retrieve a lot of information. For example, when adding type to the fields, it says that this is deprecated (I'm running python 3.7.3)
Third questions: how do I retrieve the reach for every post? I'm assuming this is obtained by scraping the insights, by I don't seem to get it right... How do I query the Graph API for those data?
In general, I'm finding a lot of trouble in just getting the right keywords while building the links. I've installed facebook-sdk but I don't know how to use it (as I said, I'm a beginner). Do you have suggestions on this?
Thanks very much to everyone answering, and greetigs from Italy!

First of all I suggest to use the latest version of the API available, currently the 5.0, regarding your question:
Second question: I cannot retrieve a lot of information. For example,
when adding type to the fields, it says that this is deprecated (I'm
running python 3.7.3)
Regarding to the doc of the Page Feed see the attachments field, as example, adding this to the request:
attachments.fields(media_type)
Third questions: how do I retrieve the reach for every post? I'm
assuming this is obtained by scraping the insights, by I don't seem to
get it right... How do I query the Graph API for those data?
Regarding to the doc of the Page Insights see the page_impressions field, as example in order to return the page_impressions field for a lifetime period:
insights.period(lifetime).metric(post_impressions_unique)
A complete example:
https://graph.facebook.com/v3.1/<PAGE-ID>/posts?fields=message,id,reactions.type(LIKE).limit(0).summary(total_count).as(reactions_like),insights.period(lifetime).metric(post_impressions_unique),attachments.fields(media_type)
Will return:
{
"data": [{
"message": "Hello",
"id": "269816000129666_780829305694997",
"reactions_like": {
"data": [],
"summary": {
"total_count": 0
}
},
"insights": {
"data": [{
"name": "post_impressions_unique",
"period": "lifetime",
"values": [{
"value": 15
}],
"title": "Lifetime Post Total Reach",
"description": "Lifetime: The number of people who had your Page's post enter their screen. Posts include statuses, photos, links, videos and more. (Unique Users)",
"id": "269816000129666_780829305694997/insights/post_impressions_unique/lifetime"
}],
"paging": {
"previous": "https://graph.facebook.com/v3.1/269816000129666_780829305694997/insights?access_token=EAAAAKq6xRNcBAOMKY3StjWXPgL1REATIfPFsyZCY21KDAnZAZB7MpKgNGCHRlKVt9bZBoVZAHpV0jqxZAAVZCOKDIh96YxvpxPaavR1AYK5EQCEEOSMKqz4ZAItcX9WvVfEEN5FzqgyoQWi8oKZBQmQB4Nf80SgicaesluNbI0hDMw2QAxfV9rAFpRc10Pop1d1vtVeziPEjEKwZDZD&metric=post_impressions_unique&period=lifetime&since=1573891200&until=1574064000",
"next": "https://graph.facebook.com/v3.1/269816000129666_780829305694997/insights?access_token=EAAAAKq6xRNcBAOMKY3StjWXPgL1REATIfPFsyZCY21KDAnZAZB7MpKgNGCHRlKVt9bZBoVZAHpV0jqxZAAVZCOKDIh96YxvpxPaavR1AYK5EQCEEOSMKqz4ZAItcX9WvVfEEN5FzqgyoQWi8oKZBQmQB4Nf80SgicaesluNbI0hDMw2QAxfV9rAFpRc10Pop1d1vtVeziPEjEKwZDZD&metric=post_impressions_unique&period=lifetime&since=1574236800&until=1574409600"
}
},
"attachments": {
"data": [{
"media_type": "photo"
}]
}
},
{
"message": "Say hello!",
"id": "269816000129666_780826782361916",
"reactions_like": {
"data": [],
"summary": {
"total_count": 0
}
},
"insights": {
"data": [{
"name": "post_impressions_unique",
"period": "lifetime",
"values": [{
"value": 14
}],
"title": "Lifetime Post Total Reach",
"description": "Lifetime: The number of people who had your Page's post enter their screen. Posts include statuses, photos, links, videos and more. (Unique Users)",
"id": "269816000129666_780826782361916/insights/post_impressions_unique/lifetime"
}],
"paging": {
"previous": "https://graph.facebook.com/v3.1/269816000129666_780826782361916/insights?access_token=EAAAAKq6xRNcBAOMKY3StjWXPgL1REATIfPFsyZCY21KDAnZAZB7MpKgNGCHRlKVt9bZBoVZAHpV0jqxZAAVZCOKDIh96YxvpxPaavR1AYK5EQCEEOSMKqz4ZAItcX9WvVfEEN5FzqgyoQWi8oKZBQmQB4Nf80SgicaesluNbI0hDMw2QAxfV9rAFpRc10Pop1d1vtVeziPEjEKwZDZD&metric=post_impressions_unique&period=lifetime&since=1573891200&until=1574064000",
"next": "https://graph.facebook.com/v3.1/269816000129666_780826782361916/insights?access_token=EAAAAKq6xRNcBAOMKY3StjWXPgL1REATIfPFsyZCY21KDAnZAZB7MpKgNGCHRlKVt9bZBoVZAHpV0jqxZAAVZCOKDIh96YxvpxPaavR1AYK5EQCEEOSMKqz4ZAItcX9WvVfEEN5FzqgyoQWi8oKZBQmQB4Nf80SgicaesluNbI0hDMw2QAxfV9rAFpRc10Pop1d1vtVeziPEjEKwZDZD&metric=post_impressions_unique&period=lifetime&since=1574236800&until=1574409600"
}
},
"attachments": {
"data": [{
"media_type": "photo"
}]
}
},

How do I pull new employee information from DocuSign through API?

Is it possible to pull the new employee’s personal information who gets hired every day from DocuSign through API? I'm trying to find a way to automate the process of user account creation process from DocuSign to Active Directory by avoiding CSV file. New to this, any input might be useful?

There are two parts to this endeavor.
The first, is not related to DocuSign. You need to get an event fired everytime a new contact is added to AD and be able to process this request. I assume you have a way to do that.
Then, the second part is using our REST API to add a new user.
Make a POST request to:
POST /v2.1/accounts/{accountId}/users
You pass this information in the request body:
{
"newUsers": [
{
"userName": "Claire Horace",
"email": "claire#example.com.com"
},
{
"userName": "Tal Mason",
"email": "tal#example.com.com",
"userSettings": [
{
"name": "canSendEnvelope",
"value": "true"
},
{
"name": "locale",
"value": "fr"
}
]
}
]
}

Python facebook-sdk: retrieving names of users who posted a message on a page

I want to use the Python facebook-sdk library to retrieve the names of the persons who posted a message on a Facebook page I created.
This is an example, returned by the Graph API explorer:
{
"feed": {
"data": [
{
"from": {
"name": "Ralph Crützen",
"id": "440590514975673"
},
"message": "Nog een test.",
"created_time": "2015-10-17T19:33:30+0000",
"id": "649463214822976_649745285127205"
},
{
"from": {
"name": "Ralph Crützen",
"id": "440590514975673"
},
"message": "Testing!",
"created_time": "2015-10-16T20:44:17+0000",
"id": "649463214822976_649492455153388"
},
... etc ...
But when I use the following Python code...
graph = facebook.GraphAPI(page_access_token)
profile = graph.get_object('tinkerlicht')
posts = graph.get_connections(profile['id'], 'feed')
print(posts['data'][0]['message'])
print(posts['data'][0]['from']['name'])
...only the message value is printed. Printing the name of the person who posted the message gives the error:
print(posts['data'][0]['from']['name'])
KeyError: 'from'
At first, I thought that I needed the read_page_mailboxes permission. To use this permission, it has to be approved by Facebook, so I submitted a request. But Facebook replied:
"You don't need any additional permissions to post to Pages or blogs that you administer. You only need to submit your app for review if your app will use a public-facing login."
So what exactly is the reason I can't retrieve the from data from the messages feed? (While reading it from the Graph API explorer works fine...)
Btw, I'm using a page access token which never expires. I generated this token the way it's described here.

Individual User Custom Search Engine with ElasticSearch/Django Haystack

We're in the process of writing a django app that lets users send private messages among themselves, as well as send message to a group, and are looking to implement a per-user customized search functionality so each user can search and view only messages they have received.
How do we offer a search experience that's customized to each user? Some messages are part of threads sent to thousands of users as part of a group, whereas others may be private messages sent between 2 users and even others may be "pending" messages that are held for moderation.
Do we hard-code the filters that determine if a user can view a message into each query we send to ElasticSearch, or if a message goes to a group with 1000 members do I add 1000 identical documents to ElasticSearch with the only thing changing being the recipient?
Update
So here's an individual message in it's serialized form serialized:
{
"snippet": "Hi All,Though Marylan...", // Friendly snippet, this will be needed in the result
"thread_id": 28719, // Unique ID for this thread
"thread_title": "Great Thread Title Here", // Title for the thread, will be used to diplay in search results
"sent_at": "2015-03-19 07:28:15.092030-05:00", // Datetime the message was originr
"text": "Clean Message Test Here", // Text to be queryable
"pending": false, // If pending, this should only appear in the search results of the sender
"id": 30580, // Unique ID for this message across the entire
"sender": {
"sender_is_staff": false, // If the sender is a staff member or not (Filterable)
"sender": "Anna M.", // Friendly name (we'll need this to display on the result page)
"sender_guid": "23234304-eeee-bbbb-1234-bfb19d56ad68" // Guid of sender (necessary to display a link to the user's profile in the result)
},
"recipient" {
"name": "", // Not filled in for group messages
"recipient_guid": "" // Not filled in for group messages
}
"type": "group", // Values for this can be 'direct' or 'group'
"group_id": 43 // This could be null
}
A user should be able to search:
All the messages that they're the "sender" of
All messages where their GUID is in the "recipient" area (and the "type" is "direct")
All the messages sent to the groups IDs they're a member of that are not pending (they could be a member of 100 groups though, so it could be [10,14,15,18,25,44,50,60,75,80,81,82,83,...])
In SQL that'd be SELECT * FROM messages WHERE text contains 'query here' AND (sender.guid = 'my-guid' OR recipient.guid = 'my-guid' OR (group_id in [10,14,15,18,25,44,50,60,75,80,81,82,83,...] AND pending != True))

I hope I'm understanding your problem correctly.
So you have a messaging system where there are 3 types of messages (group, 2-users, moderated). Your goal is to allow your users to search through all messages, with the option to apply filters on type, user, date, etc.
Take advantage of the scalable nature of ElasticSearch for storing your searchable data. First, consider the servers on which your ES nodes are running on. Do they have enough performant resources (memory, CPU, network, hard drive speed) for your traffic and the size/quantity of your documents? Once you've decided on the server specs, you can simply add more as needed to distribute data and processing.
Next, create your message document structure. I imagine your mapping may look something like this:
"message": {
"properties": {
"id": {
"type": "long"
},
"type": {
"type": "string"
},
"body": {
"type": "string"
},
"from_user": {
"type": "object",
"properties": {
"id": {
"type": "integer"
},
"name": {
"type": "string"
}
}
},
"to_user": {
"type": "object",
"properties": {
"id": {
"type": "integer"
},
"name": {
"type": "string"
}
}
},
"group": {
"type": "object",
"properties": {
"id": {
"type": "integer"
},
"name": {
"type": "string"
}
}
},
"added_on": {
"type": "date"
},
"updated_on": {
"type": "date"
},
"status_id": {
"type": "short"
}
}}
You may want to create custom analyzers for the "body" and "name" fields to customize your search results to fit your expectations. Then it's just a matter of writing queries and using filters/sorts to allow users to search globally or from/to specific users or groups.
After that, you just need to set up a bridge between your database and your ES index for syncing your messages for search. Sync frequency depends on how quickly you want messages to be made available for search.
Well, I truly hope I understood your question correctly. Otherwise, OK...