Abstract

Much attention to affective computing has focused on its alleged ability to “tap into human affects,” a trope also foundational to broader theorizations about big‐data surveillance. What remains understudied and undertheorized is affective computing's social life, where interested parties contest and collude on its deployment. This essay traces how such portable technologies as sentiment analysis and “like” buttons wound up redefining collective action in China, which partly explains the conservative turn observed in Chinese online cultures since the mid‐2010s. It unpacks affective computing's ambient politics — the fraught processes whereby social actors aggressively repackage, reinterpret, and remediate these technologies to fit their agendas, changing social standards for denoting emotions along the way. This essay calls to reorient critical analysis of affective computing away from its design epistemics to its ambient politics and, in parallel, to shift the focus from interiorized subjects to conditions of collective existence.

In August 2009, an article entitled “Mining the Web for Feelings, Not Facts” introduced readers of the New York Times to sentiment analysis, or algorithmic techniques for automatically classifying open-ended written accounts by emotion.1 While marveling at the power of sentiment analysis to ride the “bull market” of social media, “opening a tantalizing window” and “translating the vagaries of human emotion into hard data,” the author also worried that this systematic computation of emotion might sideline the use of factual processing to make decisions (Wright 2009). Citing this coverage, critical scholarship designated sentiment analysis as a primary instrument that marketing and political campaigns wielded to exploit people's emotional investments in participatory cultures (Andrejevic 2013: chap. 3). Today when we talk about affective computing, at least in Euro-American societies, we think about recognizing emotions from facial microexpressions. Sentiment analysis, albeit an expansive subfield of affective computing,2 has fallen out of the limelight. This trajectory alone should lead us to question the perennial hype about emergent technologies granting access to psychological states. That said, affective computing invites more exploration, both empirically and conceptually, that is not anchored by its capacity to capture mental activity, either treated as truism or a scientific scandal.

By affective computing I mean calculative technologies that purportedly recognize emotion based on data detailing a wide array of human behaviors. Such technologies include “like” metrics, whose nominal emotive claim heralded the arrival of the social web (Gerlitz and Helmond 2013; Gehl 2014: 88).3 I keep the definition expansive not least because in technical literature the boundary of affective computing is not conclusive (e.g., opinions differ as to whether data are restricted to physiological traces), but more importantly for conceptual interventions: seemingly diverse technologies, so long as they claim to register emotion, have consistently instigated theorization and critiques that share the same primary concern with interiority.

Critical scholarship continues to situate a series of such technologies in what is described as a longer history of the “becoming public of affect,” often entwined with the expansion of extractive capitalism (Arvidsson 2011: 49). The same vocabulary—“opening up,” “getting in,” “drilling deep,” “unexplored frontiers,” and so forth—has recurred in popular and academic literature for over a decade. The notions of “digitally tapping into human affects” and “capitalizing on emotions” have anchored early conceptualizations of sentiment analysis and “like” buttons—to which I will return—in strikingly similar manners. The same preoccupation with penetration is also foundational to recent theorizations about big-data surveillance where profit and administration presuppose the capture and modulation of human interiority (e.g., Couldry and Mejias 2019; Zuboff 2019). Amid AI fears and uncertainties, nothing can better attest to this prevalent imaginary than the popular portrayal of Chinese digital systems. As the liberal West's Other, China is deployed to fuel its darkest fantasies—as the ne plus ultra of a state able to track and discipline invasively. This essay departs from this preoccupation to center the social life of affective computing (Appadurai 1988).

In the early days of sentiment analysis in the United States, social media content was readily understood as material for sentiment analysis. Critics worried that the new digital environment, equipped with new analytical tools, would sideline evidence-based rational deliberation. Such an assumption reflects unspoken presumptions: ordinary users’ expressions are feelings, whereas news from reputable media is facts. The truth is, however, that sentiment-analysis systems “see” emotion wherever they are deployed. Once fed to sentiment analysis, news turns emotional; this was exactly what happened in the 2014 Facebook emotion study, where news and personal posts indistinguishably constituted the emotional makeup of users’ News Feed (Kramer, Guillory, and Hancock 2014). And once put under the lens of sentiment analysis, news further depreciates. It becomes another type of content automatable for “empathic optimisation,” in the words of researchers of social media disinformation (Bakir and McStay 2018). Sentiment analysis, it seems, remains enmeshed in American concerns about political polarization in ways that broadly accord with the valorization of the sensual versus the intellect vis-à-vis the democratic process since Aristotle (see Gross 2007; Rancière 2013).

Meanwhile, sentiment analysis appears to strike a different chord in the Chinese context. The COVID-19 pandemic prompted a national machine-learning contest to train models on curated datasets from Weibo, China's much-larger-than-Twitter platform. The aim was “to quickly discover people's mood swings, so that policies and directives are made with pertinence—a mission of great social value” (SMP2020-EWECT 2020). Thanks to its language-specificity, sentiment analysis has separate routes of annual patenting activity (fig. 1). Whereas the number of sentiment-analysis-related patents has dropped sharply of late in the US, Chinese patenting shot up in the mid-2010s. But rather than a lag in cutting-edge research, as this essay will show, the contour of Chinese sentiment analysis patenting suggests aspiration for entrepreneurial implementation irrespective of the technique's (in)validation within scientific communities.

The divergence between American and Chinese trajectories stems from ambient politics.4 I use the term ambient politics to refer to the processes whereby social actors contest and negotiate over affective computing's interpretation and deployment. Compared to the epistemological premises built into the technology, the ambient politics of affective computing have received relatively little attention.5 Yet the social consequences of affective computing can be political, economic, and epistemic, and they cannot be deduced from the epistemics embedded in its design features. They are instead contingent on whether and how the technology—which makes autonomous and universalist assertions about emotion—is made part of local projects, whether and how it is lodged into the symbolic-material infrastructures through which a society recognizes, valorizes, and regulates emotional expressions. To illustrate this, I interrogate how, through ambient politics emergent from diverse agendas of Internet governance, such portable technologies as sentiment analysis and “like” buttons wound up reconfiguring what collective action is about in China.6 At its heart, this essay seeks to reorient theorization of affective computing away from individualized and interiorized subjects to shared conditions for collective agency and sociality.

From Design Epistemics to Ambient Politics

Emotionology: the attitudes or standards that a society, or a definable group within a society, maintains toward basic emotions and their appropriate expression; ways that institutions reflect and encourage these attitudes in human conduct. (Stearns and Stearns 1985: 813)

In their interrogation of historical changes of emotion, social historians have advanced the term emotionology to avoid conflating actual experience of emotion with norms about emotional expressions. Loving behavior by parents is not the same as recommendations about how to behave lovingly in child-rearing manuals; actual patterns of anger cannot be assumed from a community's repression of anger through socialization. Emotionology appraises emotion and designates the location of emotional expression. It can be thought of as symbolic-material infrastructures buttressing the composition of our emotional world. These infrastructures consist of cumulative, layered technologies of perception (and occlusion) that institutions of oppression and exploitation set in place (Gross 2007; see also Ahmed 2004). They may afford the enslaved and the poor narrower emotional ranges, seek out signs of elusiveness in women (Alder 2002), and interfuse negative energy and social criticism. To conceptually differentiate the actual emotional experience of those under power is thus also politically meaningful (Reddy 1997). Investigating emotionology proper (and steering clear of interior workings) is important in its own right. Emotionology may serve as a powerful force in shaping behavior, independent of actual change in emotional levels (Stearns and Stearns 1985). It is part of what Jacques Rancière (2013: 8) calls the “regime of the sensible”: a shared “system of a priori forms determining what presents itself to sense experience,” the condition under which collective agency and communal practices are imagined.

We see a certain degree of emotion-emotionology conflation in both the critical literature on affective computing and in extant discussions of China's societal campaigns to spread “positive energy,” which serve as the backdrop for my telling of local histories of sentiment analysis and “like” buttons. Let me begin with the latter.

The Chinese phrase “positive energy” (zhengnengliang) can be traced back to July 2012, during the London Olympics torch relay, when Weibo users celebrated Chinese torchbearers for “igniting positive energy.” Soon picked up by state media and official documents, it turned into a versatile catchphrase associated with a cheerful mentality, deeds conforming to social norms, and national economic and cultural superiority (P. Yang and Tang 2018: 15 – 18). In the eyes of its critics, the discourse of positive energy epitomizes Chinese “therapeutic governance,” which fosters affective self-fashioning in service of neoliberalism (Hird 2018: 124; Chen and Wang 2020: 212; Zhang 2015: 327 – 28).

As ubiquitous as this rhetoric may be, people's internalization of state-led imperatives cannot be assumed. Observing the vernacular practices that migrants took up to “be happy” and “get by” in Shanghai's urban ruins, ethnographers remind us: “To read subjectivity directly from public discourse is not to investigate subjectivity at all” (Gregory Simon, quoted in Richaud and Amin 2020: 83; see also Reddy 1997). This point resonates with social historians’ earlier note that “there has been . . . too much temptation to assert novel emotional experience, on the basis of admittedly novel emotionology, than the facts warrant” (Stearns and Stearns 1985: 825).

The same conceptual and methodological tensions underlie interrogations of affective computing and its implications. Much critical work centers on design epistemics—that is, the various presumptions and values that are built into seemingly neutral calculations. Thereupon such work takes two routes. The first route lands on the creation of new subjects and sociality per design epistemics, in ways not dissimilar to discursive production. The interface of mood-tracking apps, for example, sometimes bespeaks the remaking of the self. Similar conflation between emotion and emotionology also extends to broader theorizations on big-data surveillance (Couldry and Mejias 2019; Zuboff 2019). This muddiness stems from the fact that the vocabulary associated with affective computing, like educational manuals from the past and the positive-energy discourse from contemporary China, revolves around constructs of emotion.7

In contrast, I advocate for approaching affective computing strictly at the level of the emotionological, which is also a prerequisite for any empirical inquiry into its ambient politics. Affective computing designates particular human behavior—whose digital traces undergo algorithmic calculation—as a venue to “display” discrete emotions, not unlike a wide spectrum of modes and systems, past and present, for emotion gauging. From embodied-experiential sympathizing to self-reports of subjects, from “superficial” gestures to visceral-physiological recordings, all of these technologies essentially proffer standards for denoting emotions (see Dror 2011). Seen in this light, included are also practices in critical humanities that, in advocating perceptual and interpretive changes, foreground feelings as being triggered, addressed, and channeled through images, architectures, connections, and rhythms.8 To investigate affective computing with an insistence on the emotion-emotionology distinction means to refrain from recognizing emotions in one's own analysis and instead focus on tracing the contours and politics of the social recognition of emotions (i.e., emotionology). This in turn avoids arguments about people's emotional life and leaves the exploration of emergent subjectivities and senses of self to socio-anthropological investigations of users and usage (e.g., Schüll 2016).

The second route taken by critical work on affective computing is to discredit its design epistemics by foregrounding scientific disputes and developers’ arbitrary choices. Techniques to recognize affect in written accounts, facial microexpressions, tones of voice, and gaits alike tend to posit universally applicable emotion taxonomies and a fixed relationship between people's inner states and their behavioral displays (see Crawford et al. 2019). Historians and anthropologists have amply challenged the existence of psychological constants; even psychologists and computer scientists disagree about measuring emotions by behavioral traces, however granular (Stark and Hoey 2021; see also Leys 2017). Yet as urgent as this line of interrogation is, we should note that affective computing's traction and hence social consequences occur outside the academy, where it is taken up as expertise, service, and product. Arguably the early twentieth-century antecedent of affective computing, the polygraph (once named the emotograph) remained popular in police investigation and employee screening for decades in the United States (but nowhere else), despite cumulative scientific evidence disputing its accuracy (Alder 2002). Likewise, Chinese sentiment analysis reigns due not to its scientific validity, let alone accuracy, but to its legitimation from political and commercial establishments (see also Wu 2020a).

While affective computing may be designed to enact—and may even succeed in enforcing—existing dominant emotionology (see, e.g., Williamson 2017), that its social life unfolds beyond design intentions points to its transformative potential. But to investigate this process, our analysis should no longer treat affective computing as a computational endeavor to capture emotions, but instead as computational media with polysemic emotional signification. Computational media are enumerative and algorithmic outputs. Being consumed by broader society, they are prone to constant remediation (also see Wernimont 2019). Wrist-wearable devices, for example, produce a form of self-knowledge for sharing and pondering (Crawford, Lingel, and Karppi 2015). Polling graphs, which hail public opinion into being (Herbst 1998), acquire variant connotations as they circulate. Yet as computational media, affective computing creates more spacious room for social actors to maneuver, because the meanings of emotion terms are extraordinarily malleable.

The construction of affective computing faces, more generally, competing established theories of emotion for model building,9 and more specifically, “confusing emotional signals” from humans whose contexts remain inscrutable. Emotional polysemy may start as a conundrum within design epistemics. But I wish to analytically invert it into an issue of strategic ambiguity that ambient politics exploits. Consider as an analogy emoji, such as the “upside-down face,” whose ambiguity makes them exceptionally appealing (fig. 2) (Auerbach 2019: 254). As media with emotional polysemy, affective computing provides streams of fodder for interested parties to repackage and remediate. These parties may conveniently shift between motivational, evaluative, and experiential connotations of emotion as a composite phenomenon, and attach new meanings to the discrete emotions that affective computing posits to measure, such as negativity, anger, and happiness.

Online Activism as Sentimental Waves

The Chinese Internet has been an eventful place. While the Chinese government seeks to prevent street demonstrations and autonomous organization (Wang and Minzner 2015), the digital domain has absorbed immense protest energy and containment efforts by authorities. Thanks to its non-institutional and extra-institutional nature, Chinese online activism, compared to its American counterpart, can appear more spontaneous and radical (G. Yang 2016: 10 – 11). These collective actions are known as “online events,” echoing the Chinese reference to offline protests as “mass events.” Having witnessed the blossoming of online activism in the 2000s, many observers noted that toward the mid-2010s, contentious events were giving way to “consensus” events conforming to the ideological mainstream (see, e.g., G. Yang 2017). Robin Wagner-Pacifici (2017: 85) writes: “Forms . . . are the matter of events.” An event is hailed out of occurrences in ongoing, everyday life by historically contingent forms such as portraits, gestures, naming conventions, and rhetorical devices. Retracing this observed historical change, I inquire into the forms that “act representationally, demonstratively, and performatively” to let events appear (and disappear) on the Chinese Internet.10 The story of Chinese sentiment analysis charts its incorporation into one such ascending form through ambient politics.

Chinese scholarship on the eventful Internet serves as our vantage point. This scholarship has two separate origins: “new media event” research and yuqing research. Dating to the 2009 “New Media Event” workshop organized by Hong Kong scholars, the former is well-known transnationally. The workshop hosted participants from mainland China and Taiwan, as well as Daniel Dayan, who had just edited a book on the Beijing Olympics as a media event. This alchemy of energy birthed a research agenda culminating in a field-defining collection published in China (Qiu and Chen 2011). Extending Dayan and Katz's (1992) typology of media events (e.g., contest, conquest, coronation), Chinese “new-media-event” research sought to categorize online collective action in terms of how its “narrative forms” relate to larger structures of power. But unlike Dayan and Katz, who focused on how state and media elites forge broadcast media events, Chinese scholars were interested in grassroots voices seizing microphones in the Internet age. Their main methods were discourse analysis, online ethnography, and interviews.

Though much obscured from international observers, the other origin of studying online events—yuqing research—soon triumphed through an outpouring of government and industry support (fig. 3). Although frequently translated as “public opinion,” yuqing more precisely refers to intelligence on shifting expressions from the masses. While leaked yuqing reports from government agencies always offer sensational peeks into China's political system (see Pan and Chen 2018; Batke and Ohlberg 2020), yuqing analytics as a genre actually pervade academic publications. Notably, however, the high frequency of keywords such as “guidance,” “management,” and “governance” in these publications alludes to their applied orientation in stability maintenance (see also Wang and Minzner 2015).

The Yuqing Monitoring Office (renamed Yuqing Data Center in 2017) of the People's Daily, the mouthpiece of the Chinese Communist Party, started releasing annual yuqing reports as early as 2008. It did so first in collaboration with the Chinese Academy of Social Sciences and then as a “data-based consultation think-tank” that sells customized yuqing insights and training (and which went public in 2012).11 In its first report, published in a high-ranking media and communication journal, the office defined yuqing events as “events most attended by netizens,” based on the total numbers of posts containing specific keywords from three Chinese bulletin board systems (BBS) (Zhu, Hu, and Sun 2008: 32). Over time, while more data sources—e.g., search engine queries, blogs, Weibo, WeChat, news media, mobile news apps—continue to be incorporated into these analyses, the convention of event-picking by keyword frequency persists in yuqing research. Over the years yuqing research has experimented with a variety of methods, utilizing, for instance, oft-crude diagnostic description or content analysis to summarize “attitudes,” expert-panel ratings along linear schemes to indicate “social pressure,” and platforms’ own metrics such as Weibo Trending, despite its mysterious algorithm.

Increasingly, these experiments are giving way to ready-made commercial yuqing software that scrapes, computes, and visualizes publicly accessible data from the “Total Web,” including but not limited to major platforms. China's yuqing industry, worth billions of RMB, has the so-called digital listening sector as its equivalent in liberal democracies (see Kotras 2020; Karpf 2016; Kreiss and McGregor 2018). Among the crucial differences between them is that while both serve a diverse clientele including digital marketers, corporations, universities, and political campaigns, the yuqing business's major patrons are government agencies. Their transactions are visible in procurement documents as early as January 2007 (Batke and Ohlberg 2020). The unverifiability of sentiment analysis can be particularly appealing to government clients. Studying 653 leaked yuqing reports that a prefectural government used for internal upward communication, researchers remark that “the overall assessment of sentiment is almost always positive” (Pan and Chen 2018: 609). Another, and directly related, difference is that yuqing analytics is attuned to crisis management, designed to spot sporadic, unexpected, transient motions online. Compared to long-term tracking to infer and symbolize public opinion, it bears more resemblance to wartime radar screening technologies (see Geoghegan 2019).

As the coupling between academic research and the yuqing industry tightens (Wu 2020a), the latter's vision techniques have become the go-to method for probing online activism. This extends far beyond scholarly knowledge. Yuqing analytics’ formulaic yields, including graphics and narration according with industrial protocols, are curiosities in wide circulation. Because of this high visibility, ordinary people have come to recognize that collectively boosting online activity (i.e., creating “yuqing crises”) may draw authorities’ attention to local injustice and cover-ups.

The epistemic shift renders visible some aspects of collective action while obscuring others. Revealed through thick descriptions and critical semiotic interpretations, “new media events” are ethnographically identified, and they unfold in interaction with existing power structures. Yuqing events, in contrast, are quantitatively emergent content clusters.12 Furthermore, yuqing analytics are wave analytics. They render a subset of the Total Web data demarcated by select keywords into a set of time-series plots. These plots show contours of different variables including discrete emotions from sentiment analysis and word frequencies derived from word-cloud analysis. The curving portions represent the events. As the intensity of content production rises and falls, the collective action comes and goes. Sentiment analysis usually occupies the most prominent panel of digital displays and the defining section of reports. As sentiment analysis converts waves of aggregate content into waves of emotion, the nature of collective action is rendered primarily emotional (fig. 4).

The “epistemic effect” of wave inscriptions in social-scientific analysis, writes Stefan Helmreich (2020: 292 – 94), is to “authorize wave lines as well as claims about waves as material-processual things in the world.” This entails disambiguating now formally “wavy phenomena” from material causation that elides the inscription techniques. Even though yuqing analysts can investigate each emotion category in the software by clicking all the way down to individual posts, these posts appear as long lists of fragments lifted out of their contexts of utterance. Sentiment-centric yuqing analytics thus disintegrate local discursive interactions to recompose a global facade of fluctuating affective curves. In addition, yuqing reports always have negative-sounding emotions, such as negativity, anger, and sadness (fig. 4), as their focal “scientific objects.” In the same timescape, the reports note actions (e.g., announcement, distraction, flooding, or—less common—censorship) taken by the implicated party. If coincidental with the inflection points of sentiment contours, especially the negative ones, these actions are considered to have altered the event's unfolding—a typical case of conjuring causality with formulaic wave inscriptions. But ultimately, wave analytics posit the passage of time as the explanatory variable. A yuqing wave reifies a purported law of nature: a yuqing event is bound to vanish, just like the next will eventually arise. As such, yuqing events are isolated, periodic repetitions of emotional outbursts that cannot build upon one another and bear no relevance to any structural transformations.13

Finally, yuqing analytics’ interpretive framework grows out of crowd psychology. In the inaugurating report, People's Daily's analysts concluded by suggesting that deviant (piancha) yuqing was driven by a “web mob” (baomin) (Zhu, Hu, and Sun 2008: 39): “It is a bizarre phenomenon of ‘collective intolerance.’ . . . Mass agitation replaced rational self-restraints. . . . In the heat of the moment, [these people] cannot look beneath the surface at all the complex social and psychological causes. They habitually rush to persecute what they think are the ‘bad guys.’ ” Training workshops, manuals, and actual yuqing reports draw on a fusion of concepts and statements from century-old crowd psychology and modern-day communication research. This knowledge universe is evident in a photo from a senior yuqing practitioner showcasing the books on his desk. Posted on his WeChat account for connecting with clients and colleagues, the photo was meant to signal professional status. Next to his mug and keyboard, the vertical stack consists of ten workplace-wellness and team-building books and a tasteful reprint of archaic sex columns from a century ago. On top of this tight stack conveniently lie four recently published translations, of Malcolm Gladwell's The Tipping Point: How Little Things Can Make a Big Difference (2000), Gustave Le Bon's Crowd: A Study of the Popular Mind (1895), Eric Hoffer's The True Believer: Thoughts on the Nature of Mass Movements (1951), and George Orwell's Animal Farm (1945). Striking as this assortment is, all items speak to the law of crowd dynamics, despite the substance of contention. They may gather for anything. The crowd's unrestrained nature makes it inherently dangerous.

“Tapping Likes” to Spread Positive Energy

“Like” metrics are arguably the most widespread computational media that are polysemic in terms of emotion. As discussed, early conceptual work around “likes” has informed theorization about more sophisticated technologies of affective computing and surveillance more broadly. Scholars designated the “like” button as Facebook's 2009 design triumph for monetizing emotion, a manifestation of Eva Illouz's (2007) “emotional capitalism” that is about harnessing the inner, subjective life of the private self (Gehl 2014: 88). The rise of the “social web,” or Web 2.0, was attributed to “the Like button's capacity to instantly metrify and intensify user affects,” which was put in contrast with the hits and links of the past “informational” Web 1.0 (Gerlitz and Helmond 2013: 1349).

This notion about unmediated tapping was complicated by later empirical studies that showcase the multifarious strategizing users bring into their decision to click “like” (Eslami et al. 2016). The semantics also matter. Experiments show that thumbs-up buttons marked with “respect” or “recommend” instead of “like” fare systematically differently (Stroud, Muddiman, and Scacco 2017). Whereas these findings problematize generalizing the “like” button's societal implications from its design premises, my history of the Chinese “like” button below focuses on what ambient politics can achieve by engaging with its polysemic affective signification.

This history is most illuminating when told in parallel, and then in entwinement, with the ascension of positive energy as a hegemonic discourse since late 2012. In China the “like” button is known as the zan button. Zan is conventionally used as a verb meaning not “like,” but “praise” or “commend.” At the end of 2012, Yaowenjiaozi (2012), an old-guard authoritative Chinese-language magazine included zan in a feature on “ten annual catchphrases.” The entry marveled that zan began to function as an adjective meaning “good.” “It became popular first online and then broke into traditional media. . . . [Its] possible origin is Taiwan, as a Taiwanese newspaper voted zan as Taiwan's annual character for 2011.” The entry failed to mention that Facebook's Chinese version, which conquered Taiwan, translated “like” into zan. Also missing was that, in China's online universe, which blocked Facebook, WeChat introduced a “like” button, a heart icon next to the character zan, to 100 million users in April 2012. It was rolled out with Moments (pengyouquan, or “friend circle”), which features updates from contacts, a function crucial to the messaging app's expansion into a social-networking platform. WeChat's user base doubled, to 200 million, in the next five months.

In January 2013, Weibo, China's (then) bona fide monopolistic social platform, introduced “likes” using a thumbs-up icon to its 500 million users. Based on various reports on “Hot Weibo posts” and numerous screenshots from the day, the “like” function was not a game changer in user interaction; its counts were typically much smaller than counts of both comments and shares (mostly with comments). A possible explanation is China's entrenched blogosphere and BBS traditions, which normalized textual forms of public participation. In fact, following its own blog platform, Sina designed Weibo to be text heavy, allowing commentary to branch out in complex hierarchical manners, in stark contrast to Twitter. Evidently, the company set hopes high on the “like” button as the external plugin to draw traffic from outside Weibo (which did not work as hoped) (Jingyu 2013). Zan found its way into the 2013 “ten catchphrases” list as well, albeit as dian-zan, meaning “tapping likes.” This time Yaowenjiaozi (2013) was unequivocal about its origin: dian-zan “came from the ‘like’ function of major social networks. . . . Now frequently used in print media, its meaning has changed to indicate assessment of sorts [dianping]. But different from assessment, dian-zan is saying good things only.” (Serious or tongue-in-cheek, this concluding clarification would become jarring in the discursive climate of 2014 and onward, after official discourse hijacked the lighthearted phrase for its positive-energy campaign.)

Following this Yaowenjiaozi release, People's Daily published an editorial, “‘Tap Likes’ for China Power,” which attempted to review China's achievements in 2013 using all the catchphrases. The editorial regarded many cadres’ existing hostility toward online parlance as unwarranted. Instead, it called on official language to embrace “netspeak.” What was implied is that linguistic fusion is instrumental to consensus building, via which “‘China power’ may consolidate, and the positive energy of reform may spurt.” Dian-zan was the recurring phrase until the final passage:

In this “wacky” [qipa] age when new things keep emerging, faced with unyielding collective endeavors to pursue dreams, anecdotes of good Samaritans, and wonders ranging from online shopping to Big Data, people “tap likes” [dian-zan] with flying fingers, joining each other [online] to keep everyone warm. . . . While “tapping likes,” let us march toward the starting line of the New Year. (Li 2013)

“Tapping likes for” (wei . . . dian-zan) has since become a fixed collocation juxtaposing a digital neologism with objects associated with state-sanctioned values. Its metaphoric usage began infesting media and scholarly commentary. Xi Jinping's (2014) New Year's address gave it a decisive push: “Without people's support, [our cadres'] work could not have been done so properly. I tap likes for our great people.” The last line was taken as the title of his speech in popular media. People's Daily later named dian-zan one of the buzzwords that Xi brought into vogue (dai huo, literally “set on fire,” also from netspeak; see fig. 5): “Xi's Address received massive likes-tapping, which shows his close proximity with the people and the genuine resonance between them” (Sheng and Wang 2015).

The Xi administration's appropriation of “like” buttons spearheaded its program to “sync” official talk with online vernaculars (see Wu 2020b: 139 – 40). But taking an epistemological-infrastructural perspective, we may recognize this as a move that alters the legibility of collective action online. In China, with measures such as real-name registration and binding mobile-phone numbers registered with national IDs, online activity is readily traceable. Stories of being contacted, warned, and penalized by security agents, often extralegally, due to web usage circulate widely. In other words, Chinese online activism entails real bodily risks.

In this context, online participation comes with a spectrum of vulnerability. As police action against “rumors” and “illegal speech” makes clear, contributing original content is the most incriminating activity, but sharing is also risky, because it indicates rumor mongering intent (Huang 2017). Relatively speaking, “tapping likes”—notably we are now concerned with content deemed destabilizing—is the safest. This spectrum unsurprisingly aligns with platforms’ own governance strategies. During the COVID outbreak, for example, the major social media network Douban disabled the share and comment functions of many posts from Wuhan, but left their “like” buttons intact. Also unsurprisingly, when evidently sensitive content surfaces (e.g., oppression of minority groups, high-level corruption, government malpractice, unjust policy), the numbers on its share counters may keep growing and those on its “like” counters may grow even faster, but its comment section remains empty, even though it is still functional. In these precarious scenes, the “like” button provides a shielding architecture akin to barricades and sunken shelters for street assemblies. It allows people to show up and gesture toward others to join, while refraining from verbalizing (see also Butler 2015).

As computational media, comment and share counters indicate the amount of attention content receives; meanwhile, the “like” metric indicates the volume of kindred sentiments expressed toward content. The sheer counts of “likes,” easily in the hundreds of thousands and sometimes millions in China's populated online space, imply the congregation of bodies amassing under a message. In a country where no images of spontaneous physical assembly are in circulation, these are stunning scenes to behold—a fascinating case of “statistical panic,” the harrowing ruptures we feel at the sight of striking numbers presented by new vision techniques (Woodward 2009: 195 – 96). As with historical precedents wherein power sought to explain away the threatening presence of novel social connections that asserted themselves, the positive-energy discourse churned out the “likes-tapping” rhetoric to offset the stressful opacity behind “like” counts.14

Affective Computing in Chinese Emotionology

On May 19, 2018, more than forty academics and corporate data scientists spoke at the “New Media Transmission of Positive Energy” symposium in Beijing, which was jointly sponsored by the Chinese Academy of Social Sciences (CASS) and Weibo. In a major presentation, CASS researcher Liu Ruisheng provided three scales:

  • (1) On the macro level, “newness”: The role of new media in the development of Chinese society amounts to positive energy.

  • (2) On the meso level, “changes”: In the past two decades, despite constant changes in media dynamics, what remains unchanged is that as long as we work with the laws of the communicative ecology, online mainstream opinion remains a terrain of positive energy.

  • (3) On the micro level, “details”: Rather than fixate on a few extreme expressions and jump to conclusions, we must conduct more scientific analysis of online opinion and upgrade our strategies of guidance and management, so that new media spread more positive energy and fulfill people's aspiration for a good life online. (Sun 2018)

Liu's speech revealed the hidden truth about positive energy from a governance perspective. That is, its ontology is contingent on evolving digital infrastructures—ever-new media, changing communicative ecologies, and persistent “scientific” wrangling over online happenings. Over the course of the 2010s, the congested ambient politics engendered by sentiment analysis and the “like” button eventually lodged these technologies into the dominant emotionology that keeps positive energy vital and viral.

On the one hand, the ways in which sentiment analysis gets repackaged and deployed in yuqing analytics accord with the broader Chinese imaginary about the crowd. From the 1970s onward, US academia largely rejected crowd psychology. Its sociologists and historians were instead keen to investigate people's deliberation in planning, organizing, and exercising self-control in temporary gatherings (McPhail 2017). But the Chinese trajectory is distinct.15 Maoist politics imbued mass gatherings with supreme agency and designated emotional excess as an expression of moral commitments (Dutton 2016). Drastic negation followed. The postsocialist era imposed a “scientific” censure of crowd mentality, buttressed by a flurry of foreign monographs (Xiao 2017: 195 – 96), like those exhibited on the yuqing practitioner's desk. It is under this emotionology that Chinese sentiment analysis has come to scaffold the narrativization of collective action online.

Importantly, sentiment analysis performs this role only when integrated into the peculiar format of yuqing analytics, which grew out of the interstitial space straddling political, economic, and academic fields. Uniform data generated by monopolistic platforms enable macroscale aggregation that dwarfs forceful local activism. Yuqing events are also cauterized once the curves plateau out, regardless of persistent struggles. The shifting sentimental waves serve as a theater of fate. At the resolution of the Total Web, human actors are dissolved; what they consider and aspire to as they plough through webs of meanings is displaced by crowd-psychology narratives. An institution of management for diverse clients, yuqing analytics strategically interpret nominally negative sentiment waves. These waves are indications sometimes of offline unrest, sometimes of opinionated disapproval, and at still other times of a lack of reason. But overall online activism is reduced to a temporary emotional discharge bound to misfire. The epistemic shift from “new media event” research to yuqing analytics, I suggest, partly explains what appeared to be a flourishing of consensus events and a decline of contentious events on the Chinese Internet.

On the other hand, the Chinese government has historically relied on instituted practices to “weave together multiple and parallel [affective] strands and flows into one big concept” to produce political outcomes (Dutton 2016: 723; see also Perry 2002). Defining “like-tapping” as emotional expressions tied to positive energy, in this sense, extends this ethos of governance into technical infrastructures. What “like” metrics imply, accordingly, became as versatile as the content of positive energy, which seamlessly glides between the feeling of happiness that needs maximization, giving approval and support, and a proclamation to serve (state-promoted) moral agendas. The changing signification of “like” metrics further led to their integration into various algorithmic regimes, which in concert facilitates the ascension of China's new affective hegemony.

Importantly, being framed as an act to spread positive energy, “like-tapping” should be analyzed as a conformist gesture rather than a manifestation of psychology—the focal point of much existing scholarship on positive energy. As Sara Ahmed (2004: 11) poignantly observes, under the veneer of emotional contagion—in our case the “spread of positive energy”—“It is the objects of emotion that circulate, rather than emotion as such. . . . Such objects become sticky, or saturated with affect, as sites of personal and social tension” (see also Ahmed 2010: chap. 1). “Tapping likes for” something thus is a gesture to openly acknowledge that thing as a positive energy object, be it chicken soup for the soul, health tips, selfless deeds, congratulatory commentary, GDP growth, military parades, or marvels from ancient dynasties.

Accompanying this has been Weibo's incorporation of “like” metrics into its curatorial algorithms, which expose people to what their social networks “like” and rank comments by the number of “likes” instead of replies received—because, notably, “inappropriate or controversial content tend to attract more replies” (Weibo 2016). Furthermore, platform channels of state media and government agencies scramble to promulgate a hodgepodge of “positive energy” clickbait to cumulate more “likes” for performance evaluation (see Lu and Pan 2021).16 Through affectively subsuming “like” buttons, the positive-energy imperative fosters a massive choreography on social media where people seek out and tap particular content, in concert with algorithmic augmentation of its exposure. This distributed deployment of affective computing, I argue, operates alongside the disciplinary measures over content creation and distribution in Chinese Internet governance (see G. Yang 2017).

Chinese sentiment analysis and “like” buttons have also jointly ushered in emotionological change that predisposes people to engage with preexisting semantic objects, rather than to create their own. Online content faces a divide-and-rule strategy with regard to its associations with dominant sociocultural and political norms. The so-called “clear and bright [qinglang] cyberspace” that official documents and speech promote consists of feel-good, positive-energy content, which verifies those who “tap likes for” it and simultaneously gets heightened and disseminated by “like-tapping.” Folded underneath the “clear and bright cyberspace” is the “negative energy” content, which attracts “web mobs” subject to wave dynamics; its multiplication, however staggering, is momentary and bound to wear out. Both “like-tapping” conformism and yuqing sentiment analytics work to hamper ordinary users’ linguistic expression. Attuned to display the intensity of people's interaction with a priori delimited content, neither renders visible the fraught process of semantic exchange, articulation, complication, and transgression. In particular, yuqing analytics invite people to hover above the context from “on high,” effectively installing an “ironic point of view” that denies any generative potential in linguistic representation (Colebrook 2004: 135).17 These latent laws of optics accord with the interest in stability and profits shared by Chinese government, platforms, and institutional content providers. They together summon, both epistemically and behaviorally, an online universe diffused with positive energy. This provides a substantial explanation for the widely observed conservative turn of Chinese online cultures since the mid-2010s (G. Yang 2017; Wu 2020b).

Conclusion

The Chinese case I present illustrates a general research strategy to investigate affective computing—at the level of the emotionological, with a focus on its ambient politics. Emotionology refers to the symbolic-material infrastructures via which society recognizes and normalizes emotion displays. This concept aids researchers to heed the professed norms and “broader affective logic” that shape any inquiry—technical, critical, and socio-anthropological—into the realm of human emotion (Dror et al. 2016: 11). Behind both computing's prevalent adoption of “inside-out” behavioralist models of mental activity (see also Binder 2020: 763), on one hand, and its prominent critics’ cherishing of individual sovereignty over the interior as a sheltered space (see Amrute 2020, reviewing Zuboff 2019), on the other, is one peculiar emotionology that upholds a liberal subject with innate emotion. In addition, that these technologies can access interior workings, on which much of their critiques rest, is also aggressively promoted by corporate and political powers wielding them in self-interest. Attending to emotionology, in this sense, may productively reorient our analysis away from a preoccupation with individual subjects or disciplinary bodies to the construction of conditions of collective existence (see also Brown 2015: 73 – 78).

A parallel analytical shift is from affective computing's design epistemics to its ambient politics. While it is indispensable to delineate the values that are built into technologies, the social consequences of these technologies hinge on whether and how they become embedded into local emotionological infrastructures. In this fraught process, affective computing is seized upon as computational media with emotional polysemy. From a vast range of behavioral traces to a standard set of discrete emotions, it renders the most bewildering and ambivalent into the most comprehensible. But it does not end here. Affective computing produces output that is also the most accommodating for a variety of social actors to recalibrate, reinterpret, valorize, and narrativize as part of their own projects. The ambient politics of affective computing, therefore, is one of infusion and amalgamation, distinct from what has been noted as the growing “pressure to simplify emotion's measure or monitoring so it can become machine-readable and machine-expressible” (Pasquale 2020: 214). It is from these maneuvers and contestations that emotionological shifts may arise, which in turn have an impact on our imaginaries and social behaviors beyond the realm of emotion. By repackaging and remediating calculative infrastructures that denote emotions, the ambient politics of affective computing serves a central nexus linking, on one side, institutions of political and economic governance, and, on the other, a social world increasingly exposing itself to datafication.

My thanks to Guobin Yang, Natasha Schüll, Elizabeth Lenaghan, three anonymous reviewers, and the Public Culture editorial board for their invaluable comments at different stages. This essay also benefits from conversations with He Bian, Yige Dong, Lily Chumley, danah boyd, Sareeta Amrute, and Melissa Gregg, as well as with audiences at the Center on Digital Culture and Society and the Center for the Study of Contemporary China at the University of Pennsylvania, the Institute of Communications Research and the Department of Communication at the University of Illinois Urbana-Champaign, Data and Society, and the annual meeting of the Society for Social Studies of Science in New Orleans, where I presented earlier versions. My fieldwork and writing were supported by the Henry Luce Foundation / ACLS Program in China Studies and the Chiang Ching-kuo Foundation.

Notes

1.

Machine-learning or dictionary-based, sentiment analysis breaks texts into smaller units and accesses their combination and frequency. In practice, it may label “anger abounds” as “negative,” “the government should step in” as “neutral,” and, were the classification not binary, a fan's exaltation over their idol's latest photoshoot, “I'm devastated. Help!” as “fear.”

2.

See, e.g., the publisher Springer's description of the series Socio-Affective Computing, edited by Amir Hussain and Erik Cambria, www.springer.com/series/13199.

3.

Facebook “likes” were also the building block of Cambridge Analytica's “psychographics” (Hindman 2018).

4.

Such divergent trajectories echo what Sheila Jasanoff (2015: 2 – 5) highlights in her research program on sociotechnical imaginaries of desirable futures—that is, the “reception” of science and technology by nonscientific actors and institutions often varies across contexts.

5.

For a related overview of the science-and-technology-studies literature, see Jasanoff's (2015) discussion on the field's relative inattention to technological systems’ interplay with local political authority, moral order, and cultural resources, in contrast to its concentration on the social construction of their formulation and materialization.

6.

My empirical analysis draws on preserved web archives, scholarly papers, media reportage, and industry and government documents in the course of the 2010s, as well as fieldwork in the online-opinion-analytics sector conducted between 2016 and 2019.

7.

Exceptions include Davies 2017 and Williamson 2017: 269, whose analyses remain focused on how technologies impose “politically and commercially desired [forms of] feelings” as integrative to the valuations of the emotions in implementation contexts.

8.

See Leys 2017 for a political critique of affect theory as a mode of analytics whose anti-intentionalism converges with post – World War II empirical approaches to the emotions. Critical “affect-recognition” scholarship also includes a diverse body of works on the “affective politics” of digital media that considers affect and emotion's promises and liabilities in civic digital culture (e.g., Dean 2010; Papacharissi 2015). What these studies share is a reasoned recognition of human emotions and how they operate (or are operated on) in digital environments. As should become clear, my approach takes a different tack.

9.

These theories variably conceptualize emotion as motivating behavior, as evaluative signals indicating judgment, as conscious feeling states, and as their hybrids (Stark and Hoey 2021).

10.

I treat digital technologies as supporting architecture that renders assembly legible—what Judith Butler (2015: 18 – 19) calls “conditions of appearance.” This angle differs from that of the main literature on how digital technologies affect politics, examining either their cost-reducing logic in collective action (e.g., Bennett and Segerberg 2012) or, conversely, their appropriation by the establishment to gain support or dampen resistance (e.g., Schradie 2019).

11.

See the Annual Report of People.cn at Shanghai Stock Exchange (2017), 12, http://static.sse.com.cn/disclosure/listedinfo/announcement/c/2018-04-17/603000_2017_n.pdf.

12.

Unlike the theory-driven typology of “new media events,” yuqing events are loosely classified by objects of attention, such as corrupted officials, business scandals, celebrity controversies, and “social conflicts” (between people).

13.

Compared to yuqing events’ confinement by the analytics’ imposition of keyword filters and timescale, hashtag activism's boundary is never set, as it is shaped by participants’ own deployment of hashtags, which includes recycling them in new contexts to constitute long-term struggles (see Bonilla and Rosa 2015).

14.

My analysis draws inspiration from Jones-Imhotep's (2017) work on the rise of sentimentalism to account for large gatherings when old social structures crumbled in late eighteenth-century France.

15.

The imports of European crowd psychology in the first half of the twentieth century led to the discovery of the Chinese crowd. Placed at the center of China's revolutionary struggles, this figure once channeled conflicting political ideals before the Communists came to power (Xiao 2017).

16.

The main index for their performance is “communicative power” (chuanboli), a phrase borrowed from Xi's 2013 speech to urge government agencies to expand their propaganda work on social media. It assigns heavy weights to the number of “likes.”

17.

This also sets yuqing waves apart from hashtag activism, whose constant supply for intertextual linkages is a source for transformative political energies (Bonilla and Rosa 2015).

References

Ahmed, Sara.
2004
.
The Cultural Politics of Emotion
.
New York
:
Routledge
.
Ahmed, Sara.
2010
.
The Promise of Happiness
.
Durham, NC
:
Duke University Press
.
Alder, Ken.
2002
. “
A Social History of Untruth: Lie Detection and Trust in Twentieth-Century America
.”
Representations
80
, no.
1
:
1
33
.
Andrejevic, Mark.
2013
.
Infoglut: How Too Much Information Is Changing the Way We Think and Know
.
New York
:
Routledge
.
Appadurai, Arjun, ed.
1988
.
The Social Life of Things: Commodities in Cultural Perspective
.
Cambridge
:
Cambridge University Press
.
Arvidsson, Adam.
2011
. “
General Sentiment: How Value and Affect Converge in the Information Economy
.”
Sociological Review
59
, no.
s2
:
39
59
.
Auerbach, David.
2019
.
Bitwise: A Life in Code
.
New York
:
Vintage
.
Bakir, Vian, and McStay, Andrew.
2018
. “
Fake News and the Economy of Emotions
.”
Digital Journalism
6
, no.
2
:
154
75
.
Batke, Jessica, and Ohlberg, Mareike.
2020
. “
Message Control: How a New For-Profit Industry Helps China's Leaders ‘Manage Public Opinion.’
ChinaFile
,
December
20
. www.chinafile.com/reporting-opinion/features/message-control-china.
Bennett, W. Lance, and Segerberg, Alexandra.
2012
. “
The Logic of Connective Action
.”
Information, Communication, and Society
15
, no.
5
:
739
68
.
Binder, Jeffrey M.
2020
. “
The Eighteenth-Century Elocution Movement and Facebook: Reading Emotion before and after the Subject
.”
Media, Culture, and Society
42
, no.
5
:
762
76
.
Bonilla, Yarimar, and Rosa, Jonathan.
2015
. “
#Ferguson: Digital Protest, Hashtag Ethnography, and the Racial Politics of Social Media in the United States
.”
American Ethnologist
42
, no.
1
:
4
17
.
Brown, Wendy.
2015
.
Undoing the Demos: Neoliberalism's Stealth Revolution
.
Cambridge, MA
:
MIT Press
.
Butler, Judith.
2015
.
Notes toward a Performative Theory of Assembly
.
Cambridge, MA
:
Harvard University Press
.
Chen, Zifeng, and Wang, Clyde Yicheng.
2020
. “
The Discipline of Happiness: The Foucauldian Use of the ‘Positive Energy’ Discourse in China's Ideological Works
.”
Journal of Current Chinese Affairs
48
, no.
2
:
201
25
.
Colebrook, Claire.
2004
.
Irony
.
Hove, UK
:
Psychology Press
.
Couldry, Nick, and Mejias, Ulises A.
2019
.
The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism
.
Palo Alto, CA
:
Stanford University Press
.
Crawford, Kate, et al
2019
.
AI Now 2019 Report
.
New York
:
AI Now Institute
. ainowinstitute.org/AI_Now_2019_Report.html.
Crawford, Kate, Lingel, Jessa, and Karppi, Tero.
2015
. “
Our Metrics, Ourselves: A Hundred Years of Self-Tracking from the Weight Scale to the Wrist Wearable Device
.”
European Journal of Cultural Studies
18
, no.
4–5
:
479
96
.
Davies, William.
2017
. “
How Are We Now? Real-Time Mood-Monitoring as Valuation
.”
Journal of Cultural Economy
10
, no.
1
:
34
48
.
Dayan, Daniel, and Katz, Elihu.
1992
.
Media Events: The Live Broadcasting of History
.
Cambridge, MA
:
Harvard University Press
.
Dean, Jodi.
2010
.
Blog Theory: Feedback and Capture in the Circuits of Drive
.
Cambridge
:
Polity
.
Dror, Otniel E.
2011
. “
Seeing the Blush: Feeling Emotions
.” In
Histories of Scientific Observation
, edited by Daston, Lorraine and Lunbeck, Elizabeth,
326
48
.
Chicago
:
University of Chicago Press
.
Dror, Otniel E., Hitzer, Bettina, Laukötter, Anja, and León-Sanz, Pilar.
2016
. “
An Introduction to History of Science and the Emotions
.”
Osiris
31
, no.
1
:
1
18
.
Dutton, Michael.
2016
. “
Cultural Revolution as Method
.”
China Quarterly
227
:
718
33
.
Eslami, Motahhare, Karahalios, Karrie, Sandvig, Christian, Vaccaro, Kristen, Rickman, Aimee, Hamilton, Kevin, and Kirlik, Alex.
2016
. “
First I ‘Like’ It, Then I Hide It: Folk Theories of Social Feeds
.” In
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
,
2371
82
.
New York
:
Association for Computing Machinery
.
Gehl, Robert W.
2014
.
Reverse Engineering Social Media: Software, Culture, and Political Economy in New Media Capitalism
.
Philadelphia
:
Temple University Press
.
Geoghegan, Bernard Dionysius.
2019
. “
An Ecology of Operations: Vigilance, Radar, and the Birth of the Computer Screen
.”
Representations
147
, no.
1
:
59
95
.
Gerlitz, Carolin, and Helmond, Anne.
2013
. “
The Like Economy: Social Buttons and the Data-Intensive Web
.”
New Media and Society
15
, no.
8
:
1348
65
.
Gross, Daniel M.
2007
.
The Secret History of Emotion: From Aristotle's Rhetoric to Modern Brain Science
.
Chicago
:
University of Chicago Press
.
Helmreich, Stefan.
2020
. “
Wave Theory ∼ Social Theory
.”
Public Culture
32
, no.
2
:
287
326
.
Herbst, Susan.
1998
.
Reading Public Opinion: How Political Actors View the Democratic Process
.
Chicago
:
University of Chicago Press
.
Hindman, Matthew.
2018
. “
How Cambridge Analytica's Facebook Targeting Model Really Worked—According to the Person Who Built It
.”
The Conversation
,
March
30
. theconversation.com/how-cambridge-analyticas-facebook-targeting-model-really-worked-according-to-the-person-who-built-it-94078.
Hird, Derek.
2018
. “
Smile Yourself Happy
.” In
Chinese Discourses on Happiness
, edited by Wielander, Gerda and Hird, Derek,
106
28
.
Hong Kong
:
Hong Kong University Press
.
Huang, Haifeng.
2017
. “
A War of (Mis)Information: The Political Effects of Rumors and Rumor Rebuttals in an Authoritarian Country
.”
British Journal of Political Science
47
, no.
2
:
283
311
.
Illouz, Eva.
2007
.
Cold Intimacies: The Making of Emotional Capitalism
.
Cambridge
:
Polity
.
Jasanoff, Sheila.
2015
. “
Future Imperfect: Science, Technology, and the Imaginations of Modernity
.” In
Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power
, edited by Jasanoff, Sheila and Kim, Sang-Hyun,
1
33
.
Chicago
:
University of Chicago Press
.
Jingyu
.
2013
. “
Xinban weibo kehuduan: Shi shihou dian ‘zan’ le
” (“Weibo Upgraded: It's Time to Tap ‘Likes’ ”).
36Kr
,
June
28
. www.36kr.com/p/1641746841601.
Jones-Imhotep, Edward.
2017
. “
The Unfailing Machine: Mechanical Arts, Sentimental Publics, and the Guillotine in Revolutionary France
.”
History of the Human Sciences
30
, no.
4
:
11
31
.
Karpf, David.
2016
.
Analytic Activism: Digital Listening and the New Political Strategy
.
Oxford
:
Oxford University Press
.
Kotras, Baptiste.
2020
. “
Opinions That Matter: The Hybridization of Opinion and Reputation Measurement in Social Media Listening Software
.”
Media, Culture, and Society
42
, no.
7–8
:
1495
1511
.
Kramer, Adam D. I., Guillory, Jamie E., and Hancock, Jeffrey T.
2014
. “
Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks
.”
Proceedings of the National Academy of Sciences of the United States of America
111
, no.
24
:
8788
90
.
Kreiss, Daniel, and McGregor, Shannon C.
2018
. “
Technology Firms Shape Political Communication: The Work of Microsoft, Facebook, Twitter, and Google with Campaigns during the 2016 U.S. Presidential Cycle
.”
Political Communication
35
, no.
2
:
155
77
.
Leys, Ruth.
2017
.
The Ascent of Affect: Genealogy and Critique
.
Chicago
:
University of Chicago Press
.
Li, Hongbing.
2013
. “
2013 reci: wei zhongguo liliang ‘dianzan’
” (“Buzzwords 2013: ‘Tap Likes’ for China Power”).
People's Daily
,
December
20
.
Lu, Yingdan, and Pan, Jennifer.
2021
. “
Capturing Clicks: How the Chinese Government Uses Clickbait to Compete for Visibility
.”
Political Communication
38
, no.
1–2
:
23
54
.
McPhail, Clark.
2017
.
The Myth of the Madding Crowd
.
New York
:
Routledge
.
Pan, Jennifer, and Chen, Kaiping.
2018
. “
Concealing Corruption: How Chinese Officials Distort Upward Reporting of Online Grievances
.”
American Political Science Review
112
, no.
3
:
602
20
.
Papacharissi, Zizi.
2015
.
Affective Publics: Sentiment, Technology, and Politics
.
Oxford
:
Oxford University Press
.
Pasquale, Frank.
2020
.
New Laws of Robotics: Defending Human Expertise in the Age of AI
.
Cambridge, MA
:
Harvard University Press
.
Perry, Elizabeth J.
2002
. “
Moving the Masses: Emotion Work in the Chinese Revolution
.”
Mobilization
7
, no.
2
:
111
28
.
Qiu, Linchuan, and Chen, Taowen, eds.
2011
.
Xinmeiti shijian yanjiu
(New Media Event Research).
Beijing
:
Renmin University Press
.
Rancière, Jacques.
2013
.
The Politics of Aesthetics
, edited and translated by Rockhill, Gabriel.
London
:
Bloomsbury
.
Reddy, William M.
1997
. “
Against Constructionism: The Historical Ethnography of Emotions
.”
Current Anthropology
38
, no.
3
:
327
51
.
Richaud, Lisa, and Amin, Ash.
2020
. “
Life amidst Rubble: Migrant Mental Health and the Management of Subjectivity in Urban China
.”
Public Culture
32
, no.
1
:
77
106
.
Schradie, Jen.
2019
.
The Revolution That Wasn't: How Digital Activism Favors Conservatives
.
Cambridge, MA
:
Harvard University Press
.
Schüll, Natasha Dow.
2016
. “
Abiding Chance: Online Poker and the Software of Self-Discipline
.”
Public Culture
28
, no.
3
:
563
92
.
Sheng, Hui, and Wang, Simin.
2015
. “
Zheliangnian, Xi Jinping daihuo de 12 ge reci
” (“Positive Energy, the Twelve Phrases Xi Jinping Set on Fire”).
People.Cn
,
February
5
. politics.people.com.cn/n/2015/0206/c99014-26519924-10.html.
SMP2020-EWECT
.
2020
. “
The Evaluation of Weibo Emotion Classification Technology
.” Ninth China National Conference on Social Media Processing, Hangzhou, September. smp2020ewect.github.io/.
Stark, Luke, and Hoey, Jesse.
2021
. “
The Ethics of Emotion in Artificial Intelligence Systems
.” In
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
,
782
93
.
New York
:
Association for Computing Machinery
.
Stearns, Peter N., and Stearns, Carol Z.
1985
. “
Emotionology: Clarifying the History of Emotions and Emotional Standards
.”
American Historical Review
90
, no.
4
:
813
36
.
Stroud, Natalie Jomini, Muddiman, Ashley, and Scacco, Joshua M.
2017
. “
Like, Recommend, or Respect? Altering Political Behavior in News Comment Sections
.”
New Media and Society
19
, no.
11
:
1727
43
.
Sun, Long.
2018
. “
‘Xinmeiti zhengnengliang chuanbo’ yantaohui zaijing juxing
” (“‘New Media Transmission of Positive Energy’ Symposium Held in Beijing”).
Chinese Academy of Social Sciences
,
May
19
. news.cssn.cn/zx/bwyc/201805/t20180519_4265147.shtml.
Wagner-Pacifici, Robin.
2017
.
What Is an Event?
Chicago
:
University of Chicago Press
.
Wang, Yuhua, and Minzner, Carl.
2015
. “
The Rise of the Chinese Security State
.”
China Quarterly
222
:
339
59
.
Weibo
.
2016
. “
User Guide on the New Commenting Function 2.0
.”
October
20
. weibo.com/ttarticle/p/show?id=2309404032707440077749.
Wernimont, Jacqueline.
2019
.
Numbered Lives: Life and Death in Quantum Media
.
Cambridge, MA
:
MIT Press
.
Williamson, Ben.
2017
. “
Moulding Student Emotions through Computational Psychology: Affective Learning Technologies and Algorithmic Governance
.”
Educational Media International
54
, no.
4
:
267
88
.
Woodward, Kathleen.
2009
.
Statistical Panic: Cultural Politics and Poetics of the Emotions
.
Durham, NC
:
Duke University Press
.
Wright, Alex.
2009
. “
Mining the Web for Feelings, Not Facts
.”
New York Times
,
August
23
.
Wu, Angela Xiao.
2020a
. “
Chinese Computing and Computing China as Global Knowledge Production
.”
Catalyst: Feminism, Theory, and Technoscience
6
, no.
2
. https://doi.org/10.28968/cftt.v6i2.34363.
Wu, Angela Xiao.
2020b
. “
The Evolution of Regime Imaginaries on the Chinese Internet
.”
Journal of Political Ideologies
25
, no.
2
:
139
61
.
Xi, Jinping.
2014
. “
Guojia zhuxi Xi Jinping fabiao 2015 nian xinnian heci
” (“New Year's Eve Address by Chairman Xi”).
XinhuaNet
,
December
31
. www.xinhuanet.com//politics/2014-12/31/c_1113846581.htm.
Xiao, Tie.
2017
.
Revolutionary Waves: The Crowd in Modern China
.
Cambridge, MA
:
Harvard University Asia Center
.
Yang, Guobin.
2016
. “
Activism
.” In
Digital Keywords
, edited by Peters, Benjamin,
1
17
.
Princeton, NJ
:
Princeton University Press
.
Yang, Guobin.
2017
. “
Killing Emotions Softly: The Civilizing Process of Online Emotional Mobilization
.”
Communication and Society
40
:
75
104
.
Yang, Peidong, and Tang, Lijun.
2018
. “
‘Positive Energy’: Hegemonic Intervention and Online Media Discourse in China's Xi Jinping Era
.”
China: An International Journal
16
, no.
1
:
1
22
.
Yaowenjiaozi
.
2012
. “
2012 nian Shida Liuxingyu
(“Top Ten Catchphrases of 2012”)
. No.
2
:
4
6
.
Yaowenjiaozi
.
2013
. “
2013 nian Shida Liuxingyu
” (“Top Ten Catchphrases of 2013”). No.
2
:
4
6
.
Zhang, Li.
2015
. “
Psychotherapy, Spirituality, and Well-Being in a Transforming Urban China
.” In
Handbook of Religion and the Asian City
, edited by van der Veer, Peter,
315
32
.
Berkeley
:
University of California Press
.
Zhu, Huaxin, Hu, Jiangchun, and Sun, Wentao.
2008
. “
2007 nian zhongguo hulianwang yuqing fenxi baogao
” (“Chinese Internet Yuqing Report, 2007”).
Jinchuanmei
, no.
2
:
31
40
.
Zuboff, Shoshana.
2019
.
The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power
.
London
:
Profile Books
.