Digital media ecologies present numerous challenges to established conceptualizations and measures of public opinion: Unbounded, trans-local publics, massively uneven participation rates, inauthentic contributions and various algorithmic distortions undermine most inferences regarding the distribution of opinions held among discernible communities, and raise important concerns about the meaning of census-style big data analyses of digital media contents. Moreover, the algorithmically structured, networked discursive spaces on digital media also erode existing notions of public opinion climates, which vary from platform to platform, or even from one user’s perspective to another. While digital media constitute a key site where public opinion is formed and negotiated, theoretical conceptualizations remain too fragmentary to effectively guide empirical public opinion research relying on digital media contents.
In the present paper, we offer a comprehensive account of public opinion in a digital media environment, which we conceptualize as an ongoing discursive process distributed over numerous digital media sites. Reconnecting to pre-digital theories of public opinion as the negotiation of publicly acceptable stances in public discourse, we examine how public opinion is negotiated in a digital information ecosystem and transmedial communications environment, discussing key implications for the at-scale analysis of digital media contents in public opinion research.
At the core of our argument, we conceptualize digital media discourse on public issues as a normative, dynamic and interactive process: By presenting opinion statements and claims in public, participating actors seek to advance, negotiate or challenge specific stances, while simultaneously positioning themselves and others as more or less legitimate and authoritative voices. Through ongoing interactions, which echo memetically across a wide range of sites and platforms, different stances emerge as dominant or marginal, consensual or contested, informing and positioning public perceptions of prevalent opinion climates, as well as any ensuing contributions to the debate. In this distributed debate, any contribution can be qualified in numerous ways – from its visibility and reach, resonance and endorsement and rejection among co-present audiences, to its distinctive positioning owing to known and observable qualities of its presentation, its author, the site, and other relevant factors. Drawing upon recent theorizing on political talk in hybrid, transmedial communication ecologies and memetic political expression in the digital age, we propose four key contingencies shaping the negotiation of public opinion in digital public spheres, namely 1) the discursive positioning of opinion expressions; 2) their socio-technical embedding into networked communication spaces and communication flows; 3) the discursive-interactive resonance of presented claims; and 4) the normative governance of public discourse within interconnected digital communication spaces. In consequence, public opinion emerges not as the linear product or aggregation of included contents, but as the outcome of an ongoing dynamic process of the public presentation, endorsement and contestation, negotiation and dissemination of opinionated discourse.
With our conceptualization, we identify avenues for empirically studying public opinion negotiation processes in digital media ecologies, both in detail and at scale. At the same time, our theoretical account foregrounds the potential of studying public opinion as collaborative negotiation of societally acceptable stances on digital media, which constitutes a key venue where public opinion is formed in contemporary hybrid communication spaces.

For voters, estimating beforehand which candidates will receive many or few votes in an upcoming election is valuable information. Probabilistic election forecasts help voters brace themselves for adverse outcomes or mobilize themselves to bring about favored ones, and constitute a critical prerequisite for strategic voting: By estimating which candidates stand a chance, voters can adjust their vote choices between multiple acceptable options, trying to maximize the impact of their vote. Especially in two-round voting systems such as the French presidential elections, tactical estimations of candidates’ chances are critical for making one’s vote count (Plutowski et al., 2020).

While strategic voting has been widely studied (e.g., Meffert et al., 2011), we know little about how voters rely on the news and other sources of information to gauge candidates’ differential chances at receiving a sufficient share of votes. In this study, we draw upon a four-wave panel survey coupled with a large-scale news content analysis to examine how French voters in the 2022 presidential elections predict candidates’ respective chances and adjust them over time to accommodate new developments. Using a longitudinal linkage-study design, which links voters’ expectations to the media contents that they are exposed to, we distinguish two main mechanisms that may explain voters’ differential probability estimates (Blais & Bodet, 2006): On the one hand, ongoing news coverage informs voters’ expectations, as journalists, pollsters, pundits and other commentators give visibility to those candidates deemed most relevant and expressly discuss their respective chances. On the other hand, voters have relatively stable, intrinsic reasons for believing in the viability of candidates’ bids based on their political party preference, para-social sympathies and other forms of motivated reasoning. Expanding the ongoing scholarly debate about the electoral effects of public opinion polling (e.g., Daoust et al., 2020), we investigate how both wishful thinking and different forms of current information inform the formation and updating of voters’ probabilistic estimates, eroding or reinforcing confidence in their intended vote choice.

Research in the area of human information interaction (HII) typically represents viewpoints on debated topics in a binary fashion, as either against or in favor of a given topic (e.g., the feminist movement). This simple taxonomy, however, greatly reduces the latent richness of viewpoints and thereby limits the potential of research and practical applications in this field. Work in the communication sciences has already demonstrated that viewpoints can be represented in much more comprehensive ways, which could enable a deeper understanding of users’ interactions with debated topics online. For instance, a viewpoint’s stance usually has a degree of strength (e.g., mild or strong), and, even if two viewpoints support or oppose something to the same degree, they may use different logics of evaluation (i.e., underlying reasons). In this paper, we draw from communication science practice to propose a novel, two-dimensional way of representing viewpoints that incorporates a viewpoint’s stance degree as well as its logic of evaluation. We show in a  case study of tweets on debated topics how our proposed viewpoint label can be obtained via crowdsourcing with acceptable reliability. By analyzing the resulting data set and conducting a user study, we further show that the two-dimensional viewpoint representation we propose allows for more meaningful analyses and diversification interventions compared to current approaches. Finally, we discuss what this novel viewpoint label implies for HII research and how obtaining it may be made cheaper in the future.