Everyone lies about porn, and everything else too

Study shows:

The popular feminist narrative would have you believe that porn is largely consumed by men, and that depictions of violent — or at least rough — sex would be a primarily male-dominated interest.

This is untrue, states researcher Seth Stephens-Davidowitz, who says that porn featuring violence against women is significantly more popular among women compared to men.

His findings might explain the popularity of the BDSM-heavy “Fifty Shades of Grey” series of novels among female readers.

Speaking to Vox in an interview about how Google data proves that most Americans lie about their sexual preferences, the researcher and author of “Everybody Lies” asserts that more women enjoy the genre compared to male porn watchers — despite common sense and politically correct claims to the contrary.

Going to the Vox article from which this was drawn, we find it gets weirder.

Among other things, Stephens-Davidowitz’s data suggests that there are more gay men in the closet than we think; that many men prefer overweight women to skinny women but are afraid to act on it; that married women are disproportionately worried their husband is gay; that a lot of straight women watch lesbian porn; and that porn featuring violence against women is more popular among women than men.

Everybody Lies, by Seth Stephens-Davidowitz, is available from Amazon. Seth Stephens-Davidowitz is a Harvard-trained economist, former Google data scientist, and New York Times writer. The last item should not deter your reading of the book. Big data is answering some questions that no one has an interest in telling the truth about.

Now, if only this kind of material could come to the attention of the Supreme Court, we might start to get some sensible rulings on pornography.

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Gerry

Read the book and aside from typical ‘leftist’ blinders on what questions are asked and how results are interpreted I found the book and extensive notes interesting – aside from the gratuitous swipes at Trump. I wrote the author which I copy below.

Thanks for the book Seth, read it end to end and thanks for all the notes at the end. It was a nice touch to not embed reference numbers throughout the book and reading through them refreshed each of the chapters. As a retired applied data person, I really envy you your access to big data and admire your facility in ‘crunching’ through it. As someone growing up with Asimov’s Foundation Trilogy, and the Hari Seldon plan, I was convinced at an early age that if I simply had enough data I could predict everything. Naive to be sure but I would have simply gotten lost in the big data world had it been around then.

That being said I do have a few quibbles – perhaps more with your editor than directly with you. However, before addressing them I need to lay out some background context for my comments.

Being Canadian I do not presume to understand the role of race in American culture or consciousness. I find it largely confusing. For example, President Obama is African-American – clearly so as his father was African and his mother American. He is identified as being “black” although logically, at least to me, he could just as legitimately be identified as being “white”. This is what confuses me: persons with a wide range of negroid racial heritage are identified as “black” even though the majority of their racial heritage may not be. Is this what is meant by the term “systemic racism” in that regardless of the quantum of negroid racial genetic heritage one is identified as “black” if one has any amount? Or does the individual get to decide and if so why, seemingly in the majority of cases, go for the “black” identity?

I also am somewhat confused about the use of the term “racism” – not just in your book but whenever it is raised in an American context. It seems to be assumed to only relate to white attitudes about blacks and others and never black attitudes about whites and others. I have lived overseas for periods of my life and my nephew married into the Chinese community. I can assure you that the dictionary definition of racism: “a belief that race is the primary determinant of human traits and capacities and that racial differences produce an inherent superiority of a particular race” is not unique to Caucasoids.

What concerns me is how one’s view on an issue, such as racism, determines what questions can be asked and what interpretations of the data can be made. For example, on pages 11 through 13 you make the case that places with white racists voted for Trump and that Clinton was hurt by “low black turnout”. Well and good, but several questions immediately occur to me which I am not sure occurred to you. One is whether you did or can do a search for a corresponding term used by blacks for whites equivalent to the term “nigger”. The next question becomes matching Clinton’s low black turnout to those areas with high levels of negative views of whites to determine if the low turnout corresponds to the google searches for the white equivalent to “nigger”. And you could overlay those on a map of voter turnout for President Obama to really net out the impact that his race had on voter turnout in those particular geographic areas.

I really liked your careful threading the needle on correlation and causation on page 208 but think that if assertions are made about selected Trump voters the other side of that same coin needs to be examined as well. That is, of course, unless it is not permitted and then we are not into data but advocacy politics which may be your point.

The indication of bias that actually made me laugh out loud was on pages 94 and 95 on the “death tax” and “estate tax” terminology. Let’s look at how this is presented: for republicans and democrats the frequency of usage of both terms is used; for newspapers (one labelled softly as “relatively liberal” while the other as “conservative”) as a ratio. Let’s use the same scale for each and see how this turns out. Republicans use “death tax” 7.9 times more frequently while Democrats use “estate tax” 5.6 times more frequently. The “relatively liberal” newspaper used the term “estate tax” more than twice the frequency of the Democrats at 13.7 times more frequently while the “conservative” paper comes in at virtually 0. Based on this data I really do not see how “the scholars” determined the tilt of both papers – except that it appears that unless one adopts a more extreme position, in terms of word usage, than the Democrats one is viewed as conservative even though the actual usage of terms is closer to the Democrat’s usage than the Republican. I think you have done a skillful job of pointing out that the labelling done by scholars is both highly suspect and extremely biased by their particular world views.

This critique is more directed to your editor I think as it has to do with the questionable and gratuitous slap at Donald Trump on page 184. The implication seems to be that Trump is anti-immigration. I get the impression he is against illegal immigration. There is nothing in what you have presented that indicates that a high proportion of illegal immigrants generate children “who go on to notable success”. I think, to be factually fair with your swipe at Trump, you need to demonstrate there is no difference in outcomes between legal and illegal immigrants in terms of next generation outcomes. Swipe at him all you want but I thought I was buying a book about big data so I am disappointed your editor did not save that for another place and time.

My last observation has to do with America’s seeming fascination with race and at the same time aversion. I was interested to see that in the discussion on loan paybacks (pages 257 to 259) there is no parsing of the data by race. Which may be an ethical issue but again, if one is going to report that referencing God is a clue that the loan won’t be paid back, I do think it matters if that is in turn determined by other factors. Whether race is part of the application process or not given other examples in the book that could be attributed by location. This may touch on what is “unaskable” and that concerns me. When only selected parts of the story that big data can tell us are revealed which support a particular world view that is very concerning. But I guess that is the point of the book.

With all the data at your disposal, and your obvious skill in parsing and summarizing it, my prayer for you is that you ask all of the questions. I understand, from personal experience, that is not always comfortable but I think intellectual honesty and personal integrity is far more important than comfort.

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