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By Angela Xiao Wu, assistant professor at New York University

紐約大學助理教授Angela Xiao Wu

This blog post comes out of a paper by Angela Xiao Wu and Harsh Taneja that offers a new take on social sciences’ ongoing embrace of platform log data by questioning their measurement conditions. The distinct nature of platform datafication is foregrounded in comparison with the longer tradition of third-party audience measurement.

這篇博客文章來自 Angela Xiao Wu 和 Harsh Taneja 的 一篇論文 ， 通過質疑它們的測量條件，為社會科學對平臺日志數據的持續接受提供了新的思路。 與第三方受眾評估的悠久傳統相比，平臺數據化的獨特性質得到了展望。

Surfing a wave of societal awe and excitement about “Big Data,” platforms formed a habit of releasing “data science” insights on what we search, like, express, purchase, obsess over, attempt to hide, and prefer to forget. These colorful graphics and juicy taglines — most notably from OKCupid and PornHub, whose data lay claims to the quirks and desires of our intimate lives — are always popular novelties to behold, ponder, and reference. If knowing ourselves through platform data is a practice of our age, it is certainly not confined to platforms themselves. Aspiring data scientists, curious programmers, vigilant data journalists, analysts of civic organizations and political campaigns, and (last but not the least) academic social scientists such as myself make up the growing field that is figuring out who we are, what we do, and how we sway in the swathes of platform data.

平臺引起了社會對“大數據”的敬畏和興奮，習慣養成了對我們搜索，表達，購買，癡迷，試圖隱藏以及寧愿忘記的事物發布“數據科學”見解的習慣。 這些色彩鮮艷的圖形和多汁的標語，尤其是來自OKCupid和PornHub的數據，它們的數據表明了我們私密生活的怪癖和渴望，這些都是新穎的新穎事物，值得注視，思考和借鑒。 如果通過平臺數據了解自己是我們時代的一種實踐，那么它肯定不僅限于平臺本身。 有抱負的數據科學家，好奇的程序員，警惕的數據記者，民間組織和政治運動的分析人員，以及(最后但并非最不重要的)像我這樣的學術社會科學家組成了一個不斷發展的領域，該領域正在弄清我們是誰，我們做什么，以及我們如何在眾多平臺數據中搖擺。

Such data can be impressive due to their unprecedented granularity and volume, as well as the fact that they are seemingly “unobtrusive” recordings of our activities when no one is watching. These apparent strengths of data for social research are outweighed by a problem in what we call the “measurement conditions”: platform data are platforms’ records of their own behavioral experimentation. Trying to know ourselves through platform data tends to yield partial and contorted accounts of human behavior that conceal platform interventions. Moreover, though increasingly produced by non-corporate actors, such knowledge accounts and narratives tend to be amenable to platform money-making and image-building.

由于這些數據的空前的粒度和數量，以及當沒有人觀看時，它們似乎對我們的活動“不干擾”的記錄，因此這些數據之所以令人印象深刻。 社會研究數據的這些明顯優勢被我們所謂的“測量條件”問題所抵消：平臺數據是平臺自身行為實驗的記錄。 試圖通過平臺數據了解自己往往會產生隱藏在平臺干預中的人類行為的部分和扭曲的描述。 此外，盡管由非企業行為者越來越多地產生這種知識，但這些敘述和敘述往往適合平臺賺錢和建立形象。

Trying to know ourselves through platform data tends to yield partial and contorted accounts of human behavior that conceal platform interventions.

試圖通過平臺數據了解自己往往會產生隱藏在平臺干預中的人類行為的部分和扭曲的描述。

To be clear, for years many have contested the ascendance of platform data as a staple in quantitative social sciences alongside conventional data collection methods, such as surveys and experiments. These contestations focus on issues about the data’s representativeness, privacy concerns, and precarious access at the mercy of platform companies. The “measurement conditions” problem, however, is entirely different. In our newly published paper, Harsh Taneja and I call for attention to the circumstances under which these data come about: what purpose does the measurement initially serve? As historians have told us, measurement — or converting parts of the social world into quantities according to some enduring instrument — is not an end in itself, but a means for managing events and coordinating actions. Measurement is thus a product of the social and institutional context (i.e., “measurement conditions”) in which it is called upon and carried out.

需要明確的是，多年來，許多人一直將平臺數據的崛起與定量社會科學以及常規數據收集方法(例如調查和實驗)一起作為定量社會科學中的主要手段來進行競爭。 這些競賽的重點是關于數據的代表性，隱私問題以及平臺公司的不確定性。 但是，“測量條件”問題完全不同。 在我最近發表的論文中 ，Harsh Taneja和我提請注意這些數據出現的情況：測量最初起什么作用？ 正如歷史學家告訴我們的那樣，測量(或根據某種持久性工具將社會世界的一部分轉換為數量)本身并不是目的，而是管理事件和協調行動的一種手段。 因此，衡量是社會和制度環境(即“衡量條件”)的產物，在此環境中需要進行衡量。

A closer look at the measurement conditions of platforms allows us to rethink the nature of platform log data: they are essentially “administrative data” that platforms generate to realize their own organizational goals, which go little beyond enlarging advertising income, harvesting intermediary fees, and attracting venture capitals. These companies track user engagements with their platforms to evaluate and showcase “product performance.” Such data analytics are integral to the iterative process whereby platforms tinker with their digital architectures in attempts to shape usage in ways that maximize profits.

仔細研究平臺的衡量條件，我們可以重新考慮平臺日志數據的性質：它們本質上是平臺為實現自己的組織目標而生成的“管理數據”，除了增加廣告收入，收取中介費和吸引風險投資。 這些公司通過其平臺跟蹤用戶參與度，以評估和展示“產品性能”。 此類數據分析是迭代過程不可或缺的部分，在此過程中，平臺將對其數字架構進行修補，以嘗試通過使利潤最大化的方式來改變使用方式。

In other words, platform log data are not “unobtrusive” recordings of human behavior out in the wild. Rather, their measurement conditions determine that they are accounts of putative user activity — “putative” in a sense that platforms are often incentivized to keep bots and other fake accounts around, because, from their standpoint, it’s always a numbers game with investors, marketers, and the actual, oft-insecure users. With calculated neglect comes calibrated nudges: platform user activity, in the first place, is induced, coaxed, and experimented on by the platform environment. From multilayered graphical organization to complex algorithmic recommendation, it is from all these platform arrangements that user activity arises. Conversely, it is to make decisions about these arrangements that platform companies measure usage.

換句話說，平臺日志數據并不是野外人類行為的“毫不干擾”記錄。 相反，他們的衡量條件確定他們是假定的用戶活動的帳戶-在某種意義上說，“經常”是指平臺經常受到激勵以保持機器人程序和其他虛假帳戶的存在，因為從他們的角度來看，這始終是與投資者，營銷商的數字游戲，以及經常不安全的實際用戶。 經過計算的疏忽帶來了經過校準的微調：首先，平臺環境會誘發，哄騙和試驗平臺用戶的活動。 從多層圖形化組織到復雜的算法推薦，正是從所有這些平臺安排中產生了用戶活動。 相反，平臺公司將根據使用情況做出決策。

Thus, it is difficult to tell to what extent the patterns emerging from platform data are about “us,” rather than testimonies to the effects of platform nudges.

因此，很難說平臺數據出現的模式在多大程度上是關于“我們”的，而不是平臺微弱效果的證詞。

Of course, when bulks of platform log data become available for inquisitive parties to crunch, platforms keep the other part of the iterative process — shifting platform arrangements aimed to nudge usage — in the dark. Thus, it is difficult to tell to what extent the patterns emerging from platform data are about “us,” rather than testimonies to the effects of platform nudges. When we are experimental subjects oblivious to platforms’ treatments on us, taking our induced behaviors as “natural” means regarding these platforms as benign, transparent vehicles for our inherent intentions, and thus obscuring their prevailing power.

當然，當大量平臺日志數據可供查詢方處理時，平臺會將重復過程的另一部分(即旨在輕推使用的平臺安排轉移到黑暗中)保留下來。 因此，很難說平臺數據出現的模式在多大程度上是關于“我們”的，而不是平臺微弱效果的證詞。 當我們是實驗對象而忽略平臺對我們的治療時，將我們的誘發行為視為“自然”意味著將這些平臺視為對我們固有意圖的良性透明工具，從而掩蓋了它們的主導力量。

Consider peeking into our innate preferences (by race, geography, and daily rhythms!) based on “patterns” that emerge from PornHub’s log data, when the site’s visual design, temporal pacing, and content curation is all about eliciting and extending the user’s state of pleasure and pleasure seeking; or using Twitter data to study the insurgent online protests during Occupy Wall Street when, due to unknown algorithmic workings, the very term failed to trend; or using Uber’s rides data to study commuting habits when Uber wields its driving force with strategies, such as price surging under the name of (predicted but unverifiable) high demand; or using YouTube, or more fantastically Netflix data, to discern media preferences when these platforms’ entire business rests on herding sequences of viewing. (Each of these platform strategies have been creatively uncovered by critical scholars.)

考慮基于PornHub日志數據中出現的“模式”來窺視我們的先天偏好(按種族，地理和日常節奏！)，此時網站的視覺設計，時間步調和內容管理都是關于激發和擴展用戶狀態的享樂和尋求享樂； 或使用Twitter數據研究“占領華爾街”期間的叛亂在線抗議活動，當時由于未知的算法工作原理，這一術語未能趨于發展 ； 或當Uber 運用策略推動其通行動力時，使用Uber的乘車數據研究通勤習慣，例如以(預計但無法驗證的)高需求的名義飆升價格； 當這些平臺的整個業務都集中在觀看序列上時，或者使用YouTube或更奇妙的Netflix數據來識別媒體偏好。 (批評學者們創造性地發現了每種平臺策略。)

…platforms’ intervention in human behavior is at once the center of platform business models and the secret that platforms strive to hide.

……平臺對人類行為的干預既是平臺業務模型的中心，又是平臺努力隱藏的秘密。

When we wind up finding human nature in platform data, we take administrative records from insulated digital experiments as expressions of humanity in our society. The data envelope a platform-shaped hole that may eschew the scrutiny of the most sophisticated computational techniques. Such a data analytic pitfall, increasingly common in data science showcases, journalistic reporting, and academic research, effectively obscures platforms’ intervention in human behavior. And platforms’ intervention in human behavior is at once the center of platform business models and the secret that platforms strive to hide.

當我們最終在平臺數據中發現人性時，我們將隔離的數字實驗中的管理記錄作為人類在社會中的表現。 數據包圍著一個平臺形的Kong，可以避免對最復雜的計算技術的審查。 這種數據分析陷阱在數據科學展示，新聞報道和學術研究中越來越普遍，有效地掩蓋了平臺對人類行為的干預。 平臺對人類行為的干預既是平臺業務模型的中心，又是平臺努力隱藏的秘密。

What are the human actions and predispositions that initially spark our curiosity? What is the kind of self-knowledge that we would cherish as a foundation for enriching our sociality, our civil and public institutions, and our democratic process? Readily resorting to platform data analytics for such knowledge risks taking platform environments as our entire world. Instead, when dealing with platform data we should aspire to “put the platforms in perspective,” foregrounding rather than obscuring their interventions in how we behave.

最初激發我們好奇心的人類行為和傾向是什么？ 我們將以什么樣的自我知識作為豐富我們的社會，我們的公民和公共機構以及我們的民主進程的基礎？ 隨便使用平臺數據分析來獲得這樣的知識風險，需要把平臺環境當作我們的整個世界。 相反，在處理平臺數據時，我們應該著眼于“透視平臺”，而不是掩蓋他們對我們行為的干預。

In this collective effort, non-corporate critical actors may find useful some of the strategies discussed in our paper.

在這種集體努力中，非企業的關鍵角色可能會發現本文討論的一些策略有用。

Angela Xiao Wu is an assistant professor in Media, Culture and Communication at New York University researching information technology, knowledge production, and political cultures.

吳小安(Angela Xiao Wu) 是紐約大學媒體，文化和傳播學的助理教授，研究信息技術，知識生產和政治文化。

翻譯自: https://points.datasociety.net/how-not-to-know-ourselves-5227c185569

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