For folks who are interested in following my work, my advice is either email or RSS. This site you should see ‘follow blog via email’ and the RSS link on the right hand side. I sometimes post a note here on crimede-coder stuff, but not always, so just do the same (RSS, or use if-this-than-that service to turn RSS into email) on that site if you want to keep abreast of all my posts.

Another way to follow my work though is on LinkedIn. So feel free to connect with me or follow my content:

• My personal page
• My crimede-coder page

I post short form blogs/reactions on occasion (plus share my other posts/work). Social media promoting your work is often cringy, but I try to post informative and technical content (and not totally vapid self-help stuff). And I write things for people to view them, so I think it is important to promote my work.

One of the most recent things I have heard a few influencers mention how embedding links directly in LinkedIn posts they think de-promotes their work. See this discussion on HackerNews, or this person’s advice for two examples.

I formed a few opinions based on my regular postings over the past year+, but impressions of things over extended periods can often be wrong. So I actually downloaded the data to see! In terms of the thing about links and being de-promoted, I don’t see that in my data at all – this is a table of impressions broken down by the domain I linked to (for domains with at least 2+ posts over the prior year):

I did notice however two different domains – youtube and newsobserver (the Raleigh newspaper) tend to not have much engagement. So it may be certain domains are not as promoted. It is of course possible that particular content was not popular (I thought my crim observations on the Mark Rober glitterbombs would be more popular, but maybe not). But I think this is a large enough sample to at least give a good hint that they are not promoted in the same way my other links are. My no URL posts have slightly less engagement than my posts to this blog or the crimede-coder site, so overall the idea that links are penalized doesn’t appear to me to be true without more conditional statements.

Data is important, as again I think impressions can be bad for things that repeatedly happen over a long period of time. So offhand I though Tue/Thu I had less engagement, so stopped posting on those days. What does the data say?

| Day | Avg Impressions | Number Posts |
----------------------------------------
| Sun |         1,860   |         32   |
| Mon |         1,370   |         44   |
| Tue |         1,220   |         35   |
| Wed |         1,273   |         41   |
| Thu |         1,170   |         34   |
| Fri |         1,039   |         39   |
| Sat |         1,602   |         38   |

The data says Sun/Sat have higher impressions, and days of the week are lower. If anything Friday is the low day, not Tuesday/Thursday.

I have had other examples of practitioners argue with me in crime analysis or academic circles in my career that strike me as similar. In that perceptions (that people strongly believed in), did not align with actual data. So I just don’t think this idea of ‘taking the average of impressions over posts over the past year’ is something that you can really know just based on passive observation. Your perceptions are likely to be dominated by a few examples, which may be off the mark. Ditto for knowing how much crime happens at a particular location, or knowing how much different things impact survival rates for gunshots.

It is definately possible that my small page experience (currently at a few over 2700 followers on LinkedIn) is not the same as the large influencers. But without looking at actual data, I don’t trust peoples instincts on aggregate metrics all that much.

Another meta LinkedIn tip (I received from Rob Fornango) is to post tall images, so when people are scrolling your content stays on the screen longer. Here is an example post from Rob’s

It is hard for me to test this though, the links on LinkedIn sometimes expand the link to bigger images and sometimes not (and sometimes I edit the image it displays as well). And I think after a while they turn them into tiny images as well. Someone tell the folks on LinkedIn to allow us to use markdown!

So I mean I could spend a full time job tinkering, but looking at the data I have at hand I don’t plan on changing much. Just posting links to my work, and having an occasional comment as well if I think it will be of interest to more people than myself. Content over micro optimization that is (since the algorithm could change tomorrow anyway).

One of the things I have debated on is buying adverts to promote my python book. I think they are just on the cusp of a net loss though given clickthrough rates and margins on my book. So for example, LinkedIn estimates if I spend $140 to promote a post, I will get 23-99 clicks. My buy rate on the site is around 5%, so that would generate 1-5 book sales. My margins are not that high on a sale, so I would not make money on that.

I have been wondering if I posted direct adverts on Reddit for the book to the learn python forum how that would go. But I think it would be much of the same as LinkedIn (too low of clickthrough to make it worth it). But if I do those tests in the future will write up a blog post on my experience!

LinkedIn I can only find how to download my stats on the company crimede-coder page, not my personal page. Here is the script I used to convert the LinkedIn short urls back to the original domains I linked, plus the analysis:

'''
python Code to parse the domains from my
crimede-coder linkedin posts
run on 7/24/2024, so only has posts
from that date through the prior year
'''

import requests
import traceback
import pandas as pd
import time
from urllib.parse import urlparse

errors = {}

def get_link(url):
    time.sleep(2)
    try:
        res = requests.get(url)
    except Exception:
        er = traceback.format_exc()
        print(f'Error message is \n\n{er}')
        return ''
    if res.ok:
        it = res.text.split()
        it = [i for i in it if i[:4] == 'href']
        rl = it[3]
    else:
        print(f'Not ok, {url}, response: {r2.reason}')
        errors[url] = res
        return ''
    return rl[6:].replace('/">','').replace('">','')

# more often than not, linkedin converts the link in the post
# to a lnkd.in short url
def get_refer(txt):
    rs = txt.split()
    rs = [i for i in rs if i[:8] == 'https://']
    if rs:
        url = rs[0]  # if more than one link, only grabs the first
        if url[:15] == 'https://lnkd.in':
            return get_link(url)
        else:
            return url
    else:
        return ''


# this is data exported from LinkedIn on my Crime De-Coder page only goes back one year
df = pd.read_excel('crime-de-coder_content_1721834275879.xls',sheet_name='All posts',header=1)

# only need to keep a few columns
keep_cols = ['Post title','Post link','Created date','Impressions','Clicks','Likes','Comments','Reposts']
df = df[keep_cols].copy()

df['url'] = df['Post title'].apply(get_refer)

def domain(url):
    if url == '':
        return 'NO URL'
    else:
        pu = urlparse(url)
        return pu.netloc

df['domain'] = df['url'].apply(domain)

# caching out file, so do not need to reget url info
df.to_csv('ParseInfo.csv',index=False)

# Can aggregate to domain
agg_stats = df.groupby('domain',as_index=False)['Impressions'].describe()
agg_stats.sort_values(by=['count','mean'],ascending=False,ignore_index=True,inplace=True)
count_cols = list(agg_stats)[1:]
agg_stats[count_cols] = agg_stats[count_cols].fillna(0).astype(int)

# This is a nice way to print/view the results in terminal
print('\n\n' + agg_stats.head(22).to_markdown() + '\n\n')