Part of what made Twitter so interesting is the diversity of argument and the townhall nature of it being the first place things show up in the feed. I’m not sure any other company or social platform could attract the same amount of hyperactive content creation users geared at news and coverage of the moment. People are arguing and I’m sure papers will soon be arriving to describe the end of social media. This weekend I’m going to spend a bit of time reading a book by Robert Putnam of “Bowling Alone” fame called “The Upswing”.
Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon and schuster.
Putnam, R. D. (2020). The upswing: How America came together a century ago and how we can do it again. Simon and Schuster.
It’s probably somewhere in the meta analysis between social capital and social media that a compelling story about why Twitter as a company would not happen today exists. During the course of this analysis you are going to receive two different lines of inquiry. First, I’ll consider the nature of Twitter and a few books related to it and silicon valley in general. Second, we will dig into some of the AI and sentiment analysis scholarly work related to that field of study to help keep the writing trajectory for the year on track.
Books have arrived to tell the stories of what happened in Silicon Valley. A lot of unlikely things happened within the borders of the space described as silicon valley. Some of them will be a part of business courses for decades to come. It truly is an interesting thing that happened where so much creativity and output happen in such a relatively small area.
Three of the books that I have enjoyed are listed below.
Bilton, N. (2014). Hatching Twitter: A true story of money, power, friendship, and betrayal. Penguin.
Frier, S. (2021). No filter: The inside story of Instagram. Simon and Schuster.
Wiener, A. (2020). Uncanny valley: A memoir. MCD.
You can zoom out a bit and grab some classic silicon valley reading like:
Isaacson, W. (2014). The innovators: How a group of inventors, hackers, geniuses and geeks created the digital revolution. Simon and Schuster.
A lot of scholars over the years have focused their attention on Twitter for a variety of purposes. You can imagine that my interest and the interest of those scholars overlap around the ideas of AI and sentiment analysis. Digital agents abound within the Twitter space and some of them are doing some type of sentiment analysis with what scholars are identifying as artificial intelligence. That second part of the equation makes me a little bit skeptical about the totality of the claims being made. We will jump right into the deep end of Google Scholar on this one anyway .
Papers from a search for “Sentiment analysis Twitter artificial intelligence” 
Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg!. In Proceedings of the international AAAI conference on web and social media (Vol. 5, No. 1, pp. 538-541). https://ojs.aaai.org/index.php/ICWSM/article/download/14185/14034
Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with applications, 40(16), 6266-6282.
Giachanou, A., & Crestani, F. (2016). Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR), 49(2), 1-41. https://arxiv.org/pdf/1601.06971.pdf
Alsaeedi, A., & Khan, M. Z. (2019). A study on sentiment analysis techniques of Twitter data. International Journal of Advanced Computer Science and Applications, 10(2). https://www.researchgate.net/profile/Abdullah-Alsaeedi/publication/331411860_A_Study_on_Sentiment_Analysis_Techniques_of_Twitter_Data/links/5c78175ba6fdcc4715a3d664/A-Study-on-Sentiment-Analysis-Techniques-of-Twitter-Data.pdf
I had considered some evaluation of searches for both “opinion mining Twitter artificial intelligence” and “artificial intelligence analysis of public attitudes” . It’s possible some papers from both of those searches show up later. Generally, all of that argument and content could be broken down into two camps of intelligence gathering related to advertising and general opinion mining geared at understanding sentiment. One divergent thread of research from those two would be some of the efforts to identify fake or astroturf content. You can imagine that flooding either fake or astroturf content could change the dynamic for advertising or sentiment analysis. Advertising to a community of bots is a rather poor use of scarce resources.
Links and thoughts:
Top 5 Tweets of the week:
What’s next for The Lindahl Letter?
Week 109: Robots in the house
Week 110: Understanding knowledge graphs
Week 111: Natural language processing
Week 112: Autonomous vehicles
Week 113: Structuring an introduction to AI ethics
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Lindahl, N. (2023). The Lindahl letter: 104 Machine Learning Posts. Lulu Press, Inc. https://www.lulu.com/shop/nels-lindahl/the-lindahl-letter-104-machine-learning-posts/ebook/product-y244ep.html