幾個值得了解的社會學研究

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我介紹的這些研究一半發表在Nature, Science或子期刊上。As rigorous as social science gets. 混知乎鍵政圈的不妨看一下

「假新聞（Fake News)"

Vosoughi, Soroush, Deb Roy, and Sinan Aral. "The spread of true and false news online." Science 359, no. 6380 (2018): 1146-1151

MIT Media Lab 底下 Social machines組的研究。學者統計了3百萬推特用戶傳播的虛假信息，分析了趨勢。值得一提的是social machines的大佬Deb Roy本人就是推特的首席研究員。所以按道理所做的推特上的研究也是領域做的最好的

「We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017.The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times.We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends,or financial information.We foundthat false news was more novel than true news,which suggests that people were more likely to share novel information.Whereas false stories inspired fear,disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust.Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it. 」

順道說一下，之前一個MIT博士說他推測推特上20%的賬戶都是機器人（他自己就編了上百個機器人賬號）。不過結合這個研究，真人賬戶傳播虛假信息的概率比機器人高。再結合下邊的研究，毛子機器人水軍和噴子農場幫川普贏的大選都是徹底的謠言。

Allcott, Hunt, and Matthew Gentzkow. "Social media and fake news in the 2016 election." Journal of Economic Perspectives 31, no. 2 (2017): 211-36.

「In the aftermath of the 2016 US presidential election, it was alleged that fake news might have been pivotal in the election of President Trump. We do not provide an assessment of this claim one way or another. 」

"民主黨和共和黨在科學上的態度」

Shi, Feng, Yongren Shi, Fedor A. Dokshin, James A. Evans, and Michael W. Macy. "Millions of online book co-purchases reveal partisan differences in the consumption of science." Nature Human Behaviour 1, no. 4 (2017): 0079.

「Passionate disagreements about climate change, stem cell research and evolution raise concerns that science has become a new battlefield in the culture wars. We used data derived from millions of online co-purchases as a behavioural indicator for whether shared interest in science bridges political differences or selective attention reinforces existing divisions. Findings reveal partisan preferences both within and across scientific disciplines. Across fields, customers for liberal or 『blue』 political books prefer basic science (for example, physics, astronomy and zoology), whereas conservative or 『red』 customers prefer applied and commercial science (for example, criminology, medicine and geophysics). Within disciplines, 『red』 books tend to be co-purchased with a narrower subset of science books on the periphery of the discipline. We conclude that the political left and right share an interest in science in general, but not science in particular. 」

「中國的收入不平等到底有多嚴重「

Xie, Yu, and Xiang Zhou. "Income inequality in today』s China." Proceedings of the National Academy of Sciences 111, no. 19 (2014): 6928-6933.

」Using multiple data sources, we establish that Chinas income inequality since 2005 has reached very high levels, with the Gini coefficient in the range of 0.53–0.55. Analyzing comparable survey data collected in 2010 in China and the United States, we examine social determinants that help explain China』s high income inequality. Our results indicate that a substantial part of China』s high income inequality is due to regional disparities and the rural-urban gap. The contributions of these two structural forces are particularly strong in China, but they play a negligible role in generating the overall income inequality in the United States, where individual-level and family-level income determinants, such as family structure and race/ethnicity, play a much larger role.my of Sciences 111, no. 19 (2014): 6928-6933.「

「明星VS吃瓜群眾的影響力」

Bakshy, Eytan, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts. "Everyones an influencer: quantifying influence on twitter." In Proceedings of the fourth ACM international conference on Web search and data mining, pp. 65-74. ACM, 2011.

Duncan Watts 應該是做社科前沿的學者里在NS發表過的東西最多的了。沃茨的團隊數了160萬個推特賬戶的7400萬條信息，發現如果做宣傳的話多選擇影響力小的賬號去傳播效果會比選擇少量影響力大的賬號傳播的影響力大。

「In light of the emphasis placed on prominent individuals as optimal vehicles for disseminating information [19], the possibility that 「ordinary in?uencers」—individuals who exert average, or even less-than-average in?uence—are under many circumstances more cost-e?ective, is intriguing」

根據這個研究 ，沃茨認為明星並沒有帶動潮流，而是潮流帶動明星。但是畢竟民眾的潮流很難量化和記載所以歷史上只留下了名人的足跡

「為什麼白左喝拿鐵？」

DellaPosta, Daniel, Yongren Shi, and Michael Macy. "Why do liberals drink lattes?." American Journal of Sociology 120, no. 5 (2015): 1473-1511.

講到了社會學傳統研究方法之隨機問卷很難排除network auto correlation的問題

"Popular accounts of 「lifestyle politics」 and 「culture wars」 suggest that political and ideological divisions extend also to leisure activities, consumption, aesthetic taste, and personal morality. Drawing on a total of 22,572 pairwise correlations from the General Social Survey (1972–2010), the authors provide comprehensive empirical support for the anecdotal accounts. Moreover, most ideological differences in lifestyle cannot be explained by demographic covariates alone. The authors propose a surprisingly simple solution to the puzzle of lifestyle politics. Computational experiments show how the self-reinforcing dynamics of homophily and influence dramatically amplify even very small elective affinities between lifestyle and ideology, producing a stereotypical world of 「latte liberals」 and 「bird-hunting conservatives」 much like the one in which we live"

「文化產品的成功到底是質量決定的還是運氣成分更多？」

Salganik, Matthew J., Peter Sheridan Dodds, and Duncan J. Watts. "Experimental study of inequality and unpredictability in an artificial cultural market." science 311, no. 5762 (2006): 854-856.

「our findings nevertheless suggest that social influence exerts an important but counterintuitive effect on cultural market formation, generating collective behavior that is reminiscentof(but not identical to)Binformation cascades[ in sequences of individuals making binary choices. On the one hand, the more information participants have regarding the decisions of others, the greater agreement they will seem to display regarding their musical preferences; thus the characteristics of success will seem predictable in retrospect. On the other hand, looking across different realizations of the same process, we see that as social influence increases, which particular products turn out to be regarded as good or bad becomes increasingly unpredictable, whether unpredictability is measured directly or in terms of quality.

「小世界理論」

大家熟知的「地球上人與人之間最多隔六個人」就是沃茨研究和驗證的

Watts, Duncan J., and Steven H. Strogatz. "Collective dynamics of 『small-world』networks." nature 393, no. 6684 (1998): 440.

「社科的發展」

Watts, Duncan J. "Should social science be more solution-oriented?." Nature Human Behaviour 1, no. 1 (2017): 0015.

Lazer, David, Alex Sandy Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis et al. "Life in the network: the coming age of computational social science." Science (New York, NY) 323, no. 5915 (2009): 721.