Has #soctwitter Died? Should I Be Sad?

Yesterday, I compared Twitter analytics across the soc accounts that I run. Traffic has collapsed substantially, down 60% – 80%. I do not think we are down overall, as our web and podcast analytics are similar.

To me, this signals the demise of #soctwitter. I might bring it up tomorrow in banter with our guests from the Peace, War, and Conflict section.

An interesting observation is what we were already down before Musk. I think #soctwitter was already in long-term decline before the Apocalypse. Musk seems more like the event the accelerated its demise. It was the point at which the group widely decided to euthanize it.

On one hand, it strikes many as just desserts, not just for Musk but also for all this engagement-inculcating stuff that flooded our timelines with clickbait.

On the other, the loss of #soctwitter is palpable. It was very helpful as a place to find grassroots opinion and debate in the discipline, and I relied on it when creating content. It was a good place to get a feel for the zeitgeist, and I can’t recapture it elsewhere yet. And ASA Connect will not be that place. It was also a great place to meet colleagues, and it generated a lot of rewarding collegial relationships.

Are you mourning the loss of Twitter? Will something better come along? What would that even involve?

The Reading Assignment Move

The “Reading Assignment” Move:

You can’t establish or refute a point of contention in real-time, so you saddle other people with lengthy reading assignments.

“If you read Smith (1947) and Jones (1955), you would know that I’m right. I recommend that you read the books.”

It is an excellent rhetorical strategy because it allows you to pose as an expert without having to show the fruits of that expertise.

Here, you do not assert the relevant points made by Smith or Jones, but rather just imply that there are great points that someone can unlock if they invest the 20 hours required to review the material.

The best part is that you can’t be refuted on the spot, and the existence of these excellent arguments to which you are alluding can’t be verified until well after the discussion is completed.

Whenever someone asserts that they are right and you are wrong based on simple reference to publications, without details of their theoretical and empirical content, you are justified in treating these as disguised ungrounded assertions.

They look like they are proving a point without doing so. If the person understands the piece, they should be able to explain it and its relevance in the context of the discussion.

As I’m sure many colleagues will agree, it is a move that gets pulled on you early in your career. You look up the first few, find a very modest hit rate with people who make these arguments, and then they become tiresome.

Metaphors in Organizational Analysis

This post discusses the use of metaphors in organizational analysis.

Organizations are extremely important, but also staggeringly complicated, facets of human behavior. Our ability to organize in groups is arguably what separates us from other animals. Individually, we are not particularly good at physical things, like being strong and fast. We may be intelligent on some level, though this intelligence is sometimes conceptualized as being good at human things. Maybe we are smart, but a lot of what we consider to be “intelligence” or “wisdom” involves socially-transmitted information, learned through decades of study made possible through communal institutions. Organizations are extremely important.

We have many reasons to want to understand and manipulate organizations. Human resources managers are charged with the task of influencing worker behavior. Business-to-business marketers are interested in how organizations make purchasing decisions. Investment market analysts are interested in identifying well-run companies. Non-profit administrators look for ways to manage scarce human resources. Economic development policy analysts are interested in understanding how government agencies can become corrupt, or cure themselves of corruption. The list could go on. The main point is that there are many, many constituencies for information that conveys a full understanding of how organizations work, and how organizations can be influenced to do some desired thing or behave in some desired way.

For content creators, an understanding of how organizations work can be helpful in designing and diagnosing problems with their production process, general operations, and working relationships. My interviews with popular podcasters suggest that most have fairly structured creative processes and maintain a range of relationships with collaborators and service providers. If they are to appraise these processes and relationships critically and with an eye towards self-improvement, it would really help to understand how organizations work.

The problem is that, at present, organizations are far too complicated for people to understand fully. We only understand organizations in narrow ways, and our ability to fully control and manipulate them is limited in many ways. In a way, our inability to fully comprehend the workings of organizations makes sense. Think of how difficult people can have to understand and control their own behavior. It is several magnitudes more difficult to understand and control one other person’s behavior. Understanding and controlling organizations involve dozens, hundreds, or even thousands of individuals, and complex webs of interactions among them. At the present state of science and knowledge, our understanding of organizations has many limitations.

Many organizational analysts deal with this complexity through the application of different metaphors that artificially reduce organizations to limited facets of their workings. This is approach is advanced in contemporary classics like Perrow’s (1979) sociology classic Complex Organizations: A Critical Essay (University of Michigan Press) or Gareth Morgan’s (1986) management bestseller, Images of the Organization (Sage).

The use of metaphors in organizational analysis is illustrated artfully by Morgan, whose explanation I will adapt below. He gives the example of explaining a complex entity, like a human being, with reference to something else, like an ant. So, I might explain human beings by saying, “They are like ants.”

Metaphor characterizing humans as if they were ants.

In drawing this metaphor — in saying that something (humans) is like something else (ants) — we are entreating your mind to find similarities between the two. We take the wide universe of characteristics or behaviors attached to humans, and reduce our set of considerations to those also possessed by ants. While it is obvious that humans are not, on whole, a lot like ants, they have commonalities. For example, both ants and humans are highly social, and are capable of operating large and organized colonies. Metaphors prompt you to see your subject in a different light, and prime your mind to recognize features of a complex phenomenon.

We might be tempted to think that the comparison neatly encapsulates the totality of humanity. You might say, “Humans are basically like ants”, in the sense that you argue that metaphor captures everything that is salient about humans. This is a risk in this kind of mental operation. The act of drawing metaphors may draw our minds to similarities, but they also present the risk that we ignore the differences and overestimate the overall similarity.

This comparison between humans and ants is useful for some purposes. Maybe it is useful for an analysis to underscore humanity’s social nature in service of an activity like urban planning or human resources management. Under other circumstances, the metaphor might not provide useful insights. Metaphors are potentially useful under some circumstances. There is no single metaphor that explains all organizational workings or problems. It is nonsense to argue that one particular metaphor is intrinsically superior to others.

In the field of organizational analysis, clusters of research have developed around several kinds of metaphors. Morgan’s and especially Perrow’s books are recommended. They involve metaphors comparing organizations to things like machines, cultures, brains, political systems, or human control systems. Organizations can be all of these things at once, but your brain cannot process all these sides at once. So you apply these filters one by one, hoping that the act of drawing comparisons evokes insights or ideas that can help you improve your living, breathing organization.

Computers Can Make Art: Introducing “AI-Generated” Images

The 2004 film I, Robot is a futuristic murder mystery about a robot-hating Chicago detective and a robot who appears to have become sapient. In the movie, the robot-hating detective (played by Will Smith) and the robot discuss the possibility that a robot mind could create art. Twenty years ago, it was more of an open question as to whether computers could create visual art. Since then, computer-generated art seems to be much more of a reality. Earlier this month, an AI-generated image won an art contest, causing a great deal of controversy. It looks like we might have underestimated the computers.

This post describes recent developments in computer-generated images.

What are “AI-Generated Images”?

These are new software platforms that specialize in the generation of novel images based on text descriptions. Consider the three images below, which I generated using the Dall-E 2 platform. I used the prompt: “A sad robot sits and smokes a cigarette in Washington Square Park at sunset.”

This software is built on a massive database of art with attached textual descriptors (as you might find on Flickr or Pinterest). Algorithms go through these databases to “learn” which visual properties are commonly associated with descriptors. So, for example, the algorithm will go through millions upon millions of pictures, and develop estimates of, say, what shapes are present in pictures of “robots”, how bodies are presented in pictures describing them as “sad”, what backgrounds are often present in “Washington Square Park”, or what colors are present “at sunset”

The program generated these images based on an “understanding” of the visual elements that people might expect when told they would see this sort of image. They produce estimates or expectations of what might be in a hypothetical image, based on what visual features are generally present in images that contain those textual descriptors.

This is different from assembling other people’s images as if they were collages. These images did not exist prior to this image generation. The person depicted to the right, whom I described as “An old man with a wrinkly face, round glasses, a warm smile, and ruby red lipstick” does not exist and did not come in this specific form from me (the author of the textual description) or the work that “inspired” it. It was generated by a predictive model that used a database of pictures to generate a guess of what would be in an actual picture described as such.

Some argue that these images are influenced by, but not necessarily reproduced from, existing images. This is an important feature of these images. If they are indeed original and seen as “inspired” (rather than stolen) from artists (which is my view), then they constitute original intellectual property that is owned by its “authors” — the person who entered the query and terms set by those created the image generating software used to generate the image. As of mid-2022, most image-generating programs give users a license to use generated images as they wish, except under particular circumstances (like the use of image generation to create pornography, to depict real people or other intellectual property, or to generate hate material). If artists ultimately convince courts or lawmakers that such “inspiration” is in fact intellectual property theft, then they may also have claims to these images.

Like any technological advancement, computer-generated images have winners and losers. At present, it is an extremely affordable way to produce visual content for content creation enterprises that do not specialize in visual content. On the other hand, it is an example of computers performing work that people once performed, and the livelihoods of artists lost.

Will computer-generated images ruin the viability of graphic artists or photographers? Not entirely. It may hurt the market for some low-hanging fruit like, say, stock photography or clip art generation. I do not believe that it can produce highly meaningful innovations in visual arts because the computer is mimicking rather truly generating something that has never been seen. I believe that our eyes will soon adjust to what these algorithms produce, and it will come to be seen as cheap art. It is also my sense that many visual artists build personal brands or followings, such that audiences see it as meaningful that a particular individual created that cultural object. I do not think those aspects of the market will be ruined, even if technological advancement will hurt some old income streams. It is clear that creators who deal in image generation will have to adapt.

How To

The easiest method is to use the Dall-E 2 platform (above). However, some may wish to install an image generation engine locally. For that, we can use Stable Diffusion.

Installing Stable Diffusion on Your Device

Device Requirements. A modern Windows computer with a GPU of at least 6 Gb of VRAM and hard drive with at least 10 Gb of free space.

Installation Method. It is possible to generate images on your own device using an open-source program called Stable Diffusion. Click here for an (untested) illustrated tutorial on how to install Stable Diffusion on your Windows device with nVidia graphics drivers. I used this method to install Stable Diffusion on my device with an AMD GPU. Two notes on this install:

  • Step 2.2 of the install depicts you as performing the operation in the project’s root folder. Ensure that your directory is set to the “diffusers-dml” subdirectory
  • Your install may be halted at Step 4 due to an error related to PyTorch. You may have a version of PyTorch for devices with NVIDIA GPUs. Check out this fix.
  • I needed to uninstall and reinstall the scipy, tokenizer, and regex packages

Generating Images

This installation renders a Python script called dml_onnx.py. To enter your image prompt, change this line in the script:


So, to generate a nice oil painting of Queens College, I might enter something like:

prompt = "Queens College in Flushing NY, Fall Day, Oil Painting, Masterpiece, Award Winning"

And I got this picture. What’s interesting is that this is not Queens College, but rather a fictitious building with an exterior fence like the one surrounding campus, and bricks like the library, and maybe a few other reminiscent features.

What makes it fun is that there are innumerable permutations of the process, so you can get something different every time by changing the random seeds. Here’s what I got on the next iteration with the exact same prompt, but by changing the random seed of the following line from 42 to 142:


And I got this:

The Dall-E 2 system (above) gives you four choices right off the bat. Here’s three that Dall-E 2 gave me for this prompt. There are more options, but we will save that for another post.

Promise and Peril

There have been amazing advancements in computer image generation. Like any technological advancement, it presents opportunities and threats. As mentioned above, the technology is becoming very impressive. It presents a threat to content creators who deal in image generation, but also a source of cheap, quality art for creators who use images to create other products. I am interested to see where the courts and legislators go on these issues.

More broadly, the march of technology is highly impressive. Computers can create original art, at least on some level. Ideas that seemed fantastic 20 or so years ago are now very close to being realities. It does not seem to be a stretch to imagine entire movies generated from prompts a few decades from now. What will happen to all those people who make movies, TV shows, and commercials?

Prof. Turner’s List of Exemplary Theses

MADASR students! Are you about to start your thesis? Want to know what a good thesis looks like?

Prof. Turner has posted a list of excellent MADASR theses on his website. These were written by excellent students who have moved on to have good careers.

Information on the Annex-Contexts Seminar Series

This post provides information about the AnnexContexts Lunchtime Seminar Series on YouTube Live, scheduled for Fall 2022. For more information, please contact Joseph Cohen at joseph.cohen@qc.cuny.edu.

Project Description and Goals

We will hold a series of YouTube Live video streams featuring a new sociology book and its associated subfield. These panels will feature a host, an author of a new sociology book, and a colleague who is in the same subfield or ASA section as the author. We will hold a 60-minute live streaming session in which the host discusses the book, its associated subfield, and their subfield’s real-world relevance.

Each session will yield: (1) one live stream session, (2) a video archive of that panel, which is suitable for use in book promo, section promo, participants’ work portfolio, or asynchronous learning resources, and (3) its audio will be repurposed and redistributed as an episode of The Annex Sociology Podcast. Sessions will be promoted through the Annex and Context communications properties, as well the communications properties of participating members, publishers, and sections.

We hope to offer authors and publishers one social media campaign to promote the book and section to academic sociology enthusiasts. By the standards of our discipline, it should be a very good platform for your book.

For ASA Sections, we hope to offer an opportunity to platform your Section and offer sharable multimedia that you can use on your social media and websites. If there is something that you would like to promote on behalf of your section, we would be happy to integrate that into your corresponding week’s programming.

What Do Participants Have To Do?

Preliminary Scheduling. We will begin by reaching out to authors and ASA Section committees to find volunteers and to consult on scheduling, panel invitations, questions/topics, and other logistical matters. Publishers should provide panelists and the host with a review copy of the book.

One Week in Advance of Your Session. The host or producer will contact you to review the discussion format and questions, and to go over technical matters. Discuss any questions with the host or producer.

Recording Day. Find a quiet room and an Internet-connected computer with a webcam, headphones, and a microphone. You will connect with us via Zoom, which will be relayed to YouTube. The Zoom session will last 30- to 90-minutes, and will usually cover five to ten predetermined discussion topics.



  1. Tell the host or producer which topics are within your wheelhouse or evoke enthusiasm from you. We want to give you the opportunity to show your “A” game.
  2. Tell the host or producer if you would like to meet via Zoom in advance to discuss the panel or practice the technology. You will perform at your best if you feel relaxed and comfortable, and we want to help.
  3. Think about discrete information or opinion items that you want to convey within 30- to 60-second increments, and look for ways to insert them within the natural flow of conversation.
  4. Think about information, concepts, theories, or messages that you would like to promote among or convey to your colleagues and the public.

Content Licensing

All content will be distributed publicly on a Creative Commons license.

Interested in Participating?

If you are interested in promoting your book or section as part of this series, please send your proposal to Joseph Cohen at joseph.cohen@qc.cuny.edu.

Early career colleagues should know that senior people routinely approach outlets to promote their work and professional identities, and so they should not be discouraged from self-nominating. You have to promote yourself in this business.

Guide to Editing Podcasts on Audacity

This post distributes copies of Joseph Cohen’s (2022) “Editing Discussion Podcasts with Audiacy”, a brief tutorial that walks users through the process of compiling and editing their podcast episode using Audacity, a freeware sound editing program. You can download a copy of the program here. The basic principles shown here can be used in other programs, like Adobe Audition.

Queens College Courses in Content Creation

These courses might not be available every semester. For more information about these courses, contact their sponsoring departments. A link to these departments is given at the bottom of this page.

Basic Concepts

  • SOC 240: Content Creation & Creative Entrepreneurship (Joseph Cohen). This is a survey that course that tries to cover various parts of the content creation field. Note that there are multiple courses with the code SOC 240, and only Prof. Joseph Cohen’s section in Fall 2022 covers these topics.

Writing & Digital Publishing

  • ENGL 210W. Introduction to Creative Writing
  • ENGL 211W. Introduction to Writing Nonfiction
  • ARTS 187. Graphic Novel
  • ARTS 192. Storyboarding & Storytelling
  • ARTS 248. Book Design and Production
  • CMLIT 336. Forms of Fiction
  • CMLIT 341. Life Writing
  • JOUR 101W. Introduction to News Reporting
  • JOUR 201. Digital Journalism
  • JOUR 202. Visual Storytelling
  • MEDST 245. Screenwriting
  • MEDST 246. Art of the Adaptation

Direction & Production

  • MEDST 200. Principles of Sound and Image
  • MEDST 241. Multimedia
  • MEDST 255. Social Media
  • MEDST 314. Directing
  • MEDST 316. Commercial Production
  • MEDST 317. Advanced Post Production


  • ARTS 157: Digital Moviemaking
  • ARTS 207. Introduction to Video Editing
  • DRAM 111. Introduction to Theater Design.
  • MEDST 240. Styles of Cinema
  • MEDST 243. Introduction to Filmmaking
  • MEDST 265. Producing Independent Movies
  • MEDST 310. Documentary Filmmaking
  • MEDST 318. Cinematography

Visual Art and Design

  • ARTH 264. History of Graphic Art
  • ARTS 190. Design Foundations
  • ARTS 191. Basic Software for Design
  • ARTS 244. Color
  • ARTS 250. Design Thinking
  • PHOTO 165. DIgital Photography
  • ARTS 195. Photoshop Basics
  • ARTS 188. Illustration
  • ARTS 211. Introduction to Adobe Illustrator
  • ARTS 257. Digital Illustration


  • MEDST 313. Creative Sound Production
  • MEDST 330. The Music Industry
  • MUSIC 314. Recording Studio Fundamentals
  • MUSIC 318. Digital Recording I

Web, Games, and Apps

  • ARTS 214. Web Design.
  • CSCI 081. Introduction to Web Programming.
  • CSCI 082. Multimedia Fundamentals and Applications
  • ARTS 172. Games Design
  • ARTS 263. App Design


  • DRAM 100. Introduction to Acting
  • DRAM 150. Introduction to Dance
  • MEDST 151. Public Speaking
  • MEDST 249. Media Performance
  • MEDST 257. Nonverbal Communication

Enterprise Management

  • BALA 200. Introduction to Entrepreneurship
  • CSCI 088. Advanced Productivity Tools for Business.
  • MEDST 250. Introduction to Media Law
  • MEDST 264. Business of Media
  • PSYCH 226. Introduction to Industrial and Organizational Psychology
  • PYSCH 362. Organization Performance Management
  • SOC 224. Complex Organizations

Audience Engagement, Marketing and Promotion

  • BALA 398. Principles of Marketing
  • DATA 334. Social Research Methods
  • BUS 334. Marketing Research
  • MEDST 222. Introduction to Public Relations
  • MEDST 260. Advertising and Marketing
  • MEDST 364. Advertising, Consumption, and Culture

Focus on Audience Groups

  • AFST 202. Introduction to Black Cultures
  • AMST 110. Introduction to American Society
  • ANTH 104. Language, Culture & Society
  • ANTH 222. Sex, Gender & Culture
  • LALS 201. Contemporary Society and Film in Latin America
  • MEDST 225. Ethnicity in American Media
  • MEDST 259. Intercultural Communication
  • URBST 113. Urban Subcultures and Lifestyles
  • WGS 101. Introduction to Women and Gender Studies
  • WGS 104. Introduction to LGBTQ Studies

Deep Dives

  • SOC 103. Sociology of Life in the United States
  • SOC 216. Social Psychology
  • AMST 212. Popular Arts in America
  • ANTH 232. Photography and the Visual World
  • ANTH 285. Sociolinguistics
  • ANTH 332. Anthropology of Memory
  • ARTH 255. Late Modern and Contemporary Art
  • ARTH 256. Contemporary Art Practices
  • CMLIT 337. Archetypes
  • ENGL 170W. Introduction to Literary Study
  • ENGL 371. Twentieth- and Twenty-First-Century Drama and Performance
  • ENGL 390. Comedy and Satire
  • LCD 105. Introduction to Psycholinguistics
  • LCD 110. Phonetics
  • MEDST 100. Media Technologies from Gutenberg to the Internet
  • MEDST 101. The Contemporary Media.
  • MEDST 103. Interpersonal Communications.
  • MEDST 110. Political Communications
  • MEDST 145. History of Broadcasting
  • MEDST 211. Introduction to Sports Television
  • MEDST 257. Nonverbal Communication
  • MEDST 262. Political Economy of Media
  • PSYCH 231. Psychology of Human Motivation
  • PSYCH 217. Life-Span Developmental Psychology
  • PSYCH 334. Development of Perception and Cognition
  • SOC 218. Mass Communication and Popular Culture

More Information from Departments. At Queens College, courses are run by their sponsoring departments. For questions about individual classes, we recommend visiting department websites or contacting department personnel:

Fall 2022 Courses Taught by Joseph Cohen

Courses taught by Prof. Joseph Cohen (Queens College, CUNY) during the Fall semester of 2022.

SOC 240: Content Creation Entrepreneurship

Students will learn the basics of operating a business or nonprofit enterprise that creates content for social media and Web 2.0 platforms (like YouTube, podcasting, Instagram, TikTok, etc.).  This course combines instruction on business management and the creative process and will give students an opportunity to develop and launch their own enterprise. 

Class is scheduled to meet on Wednesdays, 1:40 PM to 4:30 PM in KY 264.

SOC 391: Internship at Queens Podcast Lab

Students need instructor approval to enroll: send a query to joseph.cohen@qc.cuny.edu.

Students will join the Queens Podcast Lab production team to help develop public scholarship and educational programming through YouTube, podcasting, social media, and Internet-distributed multimedia. Time commitments range from 3 to 9 hours per week during the semester. Click here for more information about Credit Internships at the Queens Podcast Lab.

The class will meet virtually via Zoom at a time TBD

DATA 334: Social Research Methods

Students will learn how to use empirical research methods to develop and refine information and knowledge.  Topics include: evidence-based decision-making, scientific inference-making, developing and refining facts and theories, research project design, exploratory research, qualitative research (including focus groups, interviews, ethnography, experimental), quantitative research (including surveys, database analysis, analytics), measurement, sampling, statistical analysis, and research communication.  This course includes a practical component where students will field a pro bono research project for a real-world client.

The class will meet Mondays and Wednesdays from 10:45 AM – 11:55 AM at TBD.

DATA 793: Thesis

Students need instructor approval to enroll: send a query to joseph.cohen@qc.cuny.edu.

Click here for more information about theses with Prof. Cohen.

The class will meet virtually via Zoom at a time TBD.

Fangraphs Seasonal Pitching Data, 2000 – 2019

This data set contains seasonal pitching data from the MLB from 2000 to 2019.

Download a Zip Archive of the Data and Script (includes CSV and RDS)

Click here for an explanation of the variables

Wrangling Operation

The operation requires the following packages, particularly Bill Petti’s excellent baseballr package for wrangling MLB data:

# Download the Baseball R Package:
# devtools::install_github(repo = "BillPetti/baseballr")


First, I create a table of player identifiers from the Chadwick Baseball Bureau using get_chadwick_lu() in baseballr. These identifiers will help users merge this data table to other baseball data.

# Player Identifiers
dat.playerid <- get_chadwick_lu()
dat.playerid <- dat.playerid[c(1:7,13:15,19,25:28)]
saveRDS(dat.playerid, "Player Identifiers.RDS")
identifiers <- readRDS("Player Identifiers.RDS")

Next, I download seasonal MLB pitching performance data from Fangraphs through fg_pitch_leaders()

# Scraping Batting Data
for (i in 2000:2019){
  temp <- fg_pitch_leaders(i, i, league = "all", qual = "n", ind = 1)
  assign(paste0("fg_pitch_", i), temp)

As these are all identical versions of the same data table, just representing different years, I can stack them together using rbind():

dat.pit <- fg_pitch_2000
for (i in 2000:2019){
  temp <- get(paste0("fg_pitch_", i))
  temp <- rbind(dat.pit, temp)
  assign("dat.pit", temp)

Rename the identifier in the Fangraphs table so that it is the same as the Chadwick Bureau identifer data. I then merge the two sets so that the batting data can more readily be merged with other sources.

names(dat.pit)[1] <- paste("key_fangraphs")

# Clean Up Data Types and Sort
dat.pit$key_fangraphs <- as.numeric(dat.pit$key_fangraphs)
dat.pit$Season <- as.numeric(dat.pit$Season)
dat.pit <- arrange(dat.pit, Name, Season)

# Write data
write.csv(dat.pit, "Fangraphs Pitching Leaders 2000 - 2019.csv")
saveRDS(dat.pit, "Fangraphs Pitching Leaders 2000 - 2019.RDS")

# Clean up memory 
rm(list=ls(pattern = "fg_pitch"))

Photo Credit. By derivative work: Amineshaker (talk)Image:Nolan_Ryan_in_Atlanta.jpg: Wahkeenah – Image:Nolan_Ryan_in_Atlanta.jpg|200px, Public Domain, https://commons.wikimedia.org/w/index.php?curid=5022538

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