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I have absolutely zero sense of what this actually is. All kinds of economists estimate models on very large data using computers. Does that count or not? W

I have absolutely zero sense of what this actually is. All kinds of economists estimate models on very large data using computers. Does that count or not? What good is it specifically to “know habermas as well as feature selection” ? What problems are you solving? Maybe “the world needs an explanation of what you mean when you say computational social science” would make a nice follow up…

wsintra2022 3y ago
My gist was that the author wants the data vacuumed up by big tech to be used to continue analyzing the questions posed by social science, why do we have inequalities, why do we have class division, that sort of thing.
- galangalalgol 3y ago
  I already felt the name of the field was terrifying, conjuring up thoughts of famines and quotas for workers with some arbitrary trait the network hallucinated. If we bring big corp into it, it gets so much more scary.
- noelwelsh 3y ago
  No, that's not it. It's about using computational techniques to address questions of social science. Using big tech data is not a goal, but it will sometimes be a source of data.
dleeftink 3y ago
I don't think it's a matter of whether a certain project falls under the CSS nomer depending on the scale of the data and methods employed, but that there is as much 'sociality' to computation as there is 'science', and that studying both social and computational aspects of contemporary society and their interactions can broach insights into the 'digital stack' modern life is folded into. I gathered the quote is not about Habermas and feature selection per se, but a platitude about bridging theory and practice--both social and computational theories and practices. Knowing when, for instance, not to divvy up social features that may constitute a larger (digital) public, or conversly combining/throwing away features that may in fact constitute multiple publics or subject groupings, paves the way for building more accurate or generalisable computational social science models. Why would we want such models? Perhaps to map 'hunches' about social life that were previously only in the realm of rhetoric or simply too difficult to map before the advent of large scale data collection and computation.
chaostheory 3y ago
Glad I’m not the only one who felt this way, given all the vagueness. At the moment, social science is an oxymoron.
- dleeftink 3y ago
  I think where some contention originates from, compared to for instance the German and Dutch terms 'Wissenschaft' and 'Wetenschap', is how the Angelo-Saxon 'science' nowadays emphasises universality and systemisation, whereas the former encompass more broadly all 'practices of knowing' [0]. From this point of view, Sozialwissenschaften is less the act of pinpointing universals, but rather engaging with the plurality of social life and knowledge making. [0]: https://en.m.wikipedia.org/wiki/Wissenschaft https://en.m.wikipedia.org/wiki/Wissenschaft
  - chaostheory 3y ago
    It’s clear cut and dry. None of the social studies can replicate their theoretical models. That’s why they’re not considered a science. It has nothing to do with naming in other languages. Are they still useful? Yes, they still give us a needed picture. However, until we can build a near perfect simulation of the world at the atomic level, social studies will be likely remain as an alchemy or astrology instead of a chemistry or astronomy. There are just too many variables not being accounted for and they depend on too many other disciplines that are also still relatively young like computer science and chaos theory.
    - dleeftink 3y ago
      From this view, science (singular) is only achieved or perfected once reality can be (nearly) mapped 1:1, which, as we know, is not a realistic goal ("all models are wrong, some are useful"). Beyond replication, wissenschaften (plural) additionally encompasses descriptive methods, which is less about predicting social dynamics and more about summarising sociality to a certain level of achievable granularity. Hence why I brought up the difference and why I think language differences are important--the ways in which scientists, academics and researchers understand practices of knowing and knowledge building informs their scientific process and the models of reality they hold as true. Besides, models frequently 'drift' and are rarely cut and dry[0], just as blanketing all social science models as irreplicable is simply not true (see Lotka's Law or the Barabási–Albert model; statisticians contributing greatly to social science) [1][2]. More problematically, and not necessarily directed to your views alone, is the continued 'dunking' on 'non-scientific' disciplines. Why are humanist methods held to indefinite scrutiny, while similar criticism could as easily be directed elsewhere? For instance, dismissing physics as 'not a science' because models fail to predict quantum phenomena, mathematics as 'not a science' because there are limits to provability, machine learning as 'not a science' because source corpora are subjectively chosen, and so on (just examples, not my critiques of them). It is precisely why I regard computational social science as important, seeking to bridge the seeming incompatibilities between exact and inexact phenomena. Even after more than a century of social science, we are only at the cusp of understanding the foundational patterns that make up social life, with contemporary computational methods and data collection capabilities offering new levels of detail into phenomena that were previously difficult to investigate. [0]: https://en.wikipedia.org/wiki/Concept_drift https://en.wikipedia.org/wiki/Concept_drift [1]: https://en.wikipedia.org/wiki/Lotka%27s_law https://en.wikipedia.org/wiki/Lotka%27s_law [2]: https://en.wikipedia.org/wiki/Barabási–Albert_model https://en.wikipedia.org/wiki/Barabási–Albert_model
rapjr9 3y ago
I worked with a sociologist on a variety of projects which used sensors and cellphones to do computational social science. In the past, sociology was based on observing people, writing those observations down, and then thinking about them. Some statistics were gathered, but it was a very fuzzy science. Now, electronic devices and networks have made detailed big data about people available and sociologists are using it. Electronic surveys have replaced paper surveys and greatly increased scale. This happens mostly behind the scenes, such as research on the use of electronic medical records, marketing, employee motivation, etc. Take a look at her research projects here (and look up the people she worked with to see what else they are working on): https://sph.umich.edu/faculty-profiles/anthony-denise.html https://sph.umich.edu/faculty-profiles/anthony-denise.html One of the things we did was build survey apps for phones which would sense activity and only pop up a question when a person wasn't doing much. The sensor data would also serve as a ground truth, telling us for example how much activity a person really did, versus how much they think they did. One early result from such research was that people think they are always doing things, rushing around, when the truth is they are sitting doing little for 95% to 99% of the day. We used the survey app to build a system to train a gait recognition machine learning algorithm for user authentication, where the survey app collected both sensor data and quick answers about what a person thought they were doing, where they were, were they walking, where they were... A lot of todays AI projects are essentially computational sociology, algorithms trained on peoples behavior that is used for predictions. Business has bought up a lot of the sociology talent, to work with marketing, motivating employees, and changing peoples minds (somebody had to design those motivational posters in the hallways and some of them were based on sociology, lobbying campaigns to change public opinion are based on sociology, political campaigns use sociologists work). I have a strong suspicion that all the Big Tech companies that collect lots of intimate data about people are employing sociologists to analyze that data and use it for many purposes, mostly related to advertising and motivating people to buy stuff, but also swaying opinion, making market decisions, investing, hiring and firing, and more. A common refrain in economics is that markets are unpredictable because you can't know what all the individual players are thinking, well now you can measure them and build models/AI. Big computational social science already exists at scale. I wonder what is being done with the health data from fitness trackers. Similar things have been happening in economics. Unfortunately in both cases the data is being used mainly to gain special private knowledge about the social behavior of people, rather than informing the public or public policy. Because money. There are a few researchers who are more interested in the public good though.

wsintra2022 3y ago

My gist was that the author wants the data vacuumed up by big tech to be used to continue analyzing the questions posed by social science, why do we have inequalities, why do we have class division, that sort of thing.

galangalalgol 3y ago
I already felt the name of the field was terrifying, conjuring up thoughts of famines and quotas for workers with some arbitrary trait the network hallucinated. If we bring big corp into it, it gets so much more scary.
noelwelsh 3y ago
No, that's not it. It's about using computational techniques to address questions of social science. Using big tech data is not a goal, but it will sometimes be a source of data.

dleeftink 3y ago

I don't think it's a matter of whether a certain project falls under the CSS nomer depending on the scale of the data and methods employed, but that there is as much 'sociality' to computation as there is 'science', and that studying both social and computational aspects of contemporary society and their interactions can broach insights into the 'digital stack' modern life is folded into. I gathered the quote is not about Habermas and feature selection per se, but a platitude about bridging theory and practice--both social and computational theories and practices. Knowing when, for instance, not to divvy up social features that may constitute a larger (digital) public, or conversly combining/throwing away features that may in fact constitute multiple publics or subject groupings, paves the way for building more accurate or generalisable computational social science models. Why would we want such models? Perhaps to map 'hunches' about social life that were previously only in the realm of rhetoric or simply too difficult to map before the advent of large scale data collection and computation.

chaostheory 3y ago

Glad I’m not the only one who felt this way, given all the vagueness. At the moment, social science is an oxymoron.

dleeftink 3y ago
I think where some contention originates from, compared to for instance the German and Dutch terms 'Wissenschaft' and 'Wetenschap', is how the Angelo-Saxon 'science' nowadays emphasises universality and systemisation, whereas the former encompass more broadly all 'practices of knowing' [0]. From this point of view, Sozialwissenschaften is less the act of pinpointing universals, but rather engaging with the plurality of social life and knowledge making. [0]: https://en.m.wikipedia.org/wiki/Wissenschaft https://en.m.wikipedia.org/wiki/Wissenschaft
- chaostheory 3y ago
  It’s clear cut and dry. None of the social studies can replicate their theoretical models. That’s why they’re not considered a science. It has nothing to do with naming in other languages. Are they still useful? Yes, they still give us a needed picture. However, until we can build a near perfect simulation of the world at the atomic level, social studies will be likely remain as an alchemy or astrology instead of a chemistry or astronomy. There are just too many variables not being accounted for and they depend on too many other disciplines that are also still relatively young like computer science and chaos theory.
  - dleeftink 3y ago
    From this view, science (singular) is only achieved or perfected once reality can be (nearly) mapped 1:1, which, as we know, is not a realistic goal ("all models are wrong, some are useful"). Beyond replication, wissenschaften (plural) additionally encompasses descriptive methods, which is less about predicting social dynamics and more about summarising sociality to a certain level of achievable granularity. Hence why I brought up the difference and why I think language differences are important--the ways in which scientists, academics and researchers understand practices of knowing and knowledge building informs their scientific process and the models of reality they hold as true. Besides, models frequently 'drift' and are rarely cut and dry[0], just as blanketing all social science models as irreplicable is simply not true (see Lotka's Law or the Barabási–Albert model; statisticians contributing greatly to social science) [1][2]. More problematically, and not necessarily directed to your views alone, is the continued 'dunking' on 'non-scientific' disciplines. Why are humanist methods held to indefinite scrutiny, while similar criticism could as easily be directed elsewhere? For instance, dismissing physics as 'not a science' because models fail to predict quantum phenomena, mathematics as 'not a science' because there are limits to provability, machine learning as 'not a science' because source corpora are subjectively chosen, and so on (just examples, not my critiques of them). It is precisely why I regard computational social science as important, seeking to bridge the seeming incompatibilities between exact and inexact phenomena. Even after more than a century of social science, we are only at the cusp of understanding the foundational patterns that make up social life, with contemporary computational methods and data collection capabilities offering new levels of detail into phenomena that were previously difficult to investigate. [0]: https://en.wikipedia.org/wiki/Concept_drift https://en.wikipedia.org/wiki/Concept_drift [1]: https://en.wikipedia.org/wiki/Lotka%27s_law https://en.wikipedia.org/wiki/Lotka%27s_law [2]: https://en.wikipedia.org/wiki/Barabási–Albert_model https://en.wikipedia.org/wiki/Barabási–Albert_model

rapjr9 3y ago

I worked with a sociologist on a variety of projects which used sensors and cellphones to do computational social science. In the past, sociology was based on observing people, writing those observations down, and then thinking about them. Some statistics were gathered, but it was a very fuzzy science. Now, electronic devices and networks have made detailed big data about people available and sociologists are using it. Electronic surveys have replaced paper surveys and greatly increased scale. This happens mostly behind the scenes, such as research on the use of electronic medical records, marketing, employee motivation, etc. Take a look at her research projects here (and look up the people she worked with to see what else they are working on): https://sph.umich.edu/faculty-profiles/anthony-denise.html https://sph.umich.edu/faculty-profiles/anthony-denise.html One of the things we did was build survey apps for phones which would sense activity and only pop up a question when a person wasn't doing much. The sensor data would also serve as a ground truth, telling us for example how much activity a person really did, versus how much they think they did. One early result from such research was that people think they are always doing things, rushing around, when the truth is they are sitting doing little for 95% to 99% of the day. We used the survey app to build a system to train a gait recognition machine learning algorithm for user authentication, where the survey app collected both sensor data and quick answers about what a person thought they were doing, where they were, were they walking, where they were... A lot of todays AI projects are essentially computational sociology, algorithms trained on peoples behavior that is used for predictions. Business has bought up a lot of the sociology talent, to work with marketing, motivating employees, and changing peoples minds (somebody had to design those motivational posters in the hallways and some of them were based on sociology, lobbying campaigns to change public opinion are based on sociology, political campaigns use sociologists work). I have a strong suspicion that all the Big Tech companies that collect lots of intimate data about people are employing sociologists to analyze that data and use it for many purposes, mostly related to advertising and motivating people to buy stuff, but also swaying opinion, making market decisions, investing, hiring and firing, and more. A common refrain in economics is that markets are unpredictable because you can't know what all the individual players are thinking, well now you can measure them and build models/AI. Big computational social science already exists at scale. I wonder what is being done with the health data from fitness trackers. Similar things have been happening in economics. Unfortunately in both cases the data is being used mainly to gain special private knowledge about the social behavior of people, rather than informing the public or public policy. Because money. There are a few researchers who are more interested in the public good though.