This study examines how Norwegian political parties used negative, positive or neutral sentiment on Facebook over a 10-year period (2016–2025) and how such sentiment affected the sharing of their posts. Drawing on a full population of posts from all major parliamentary parties, the study investigates (a) whether negative sentiment increases over time, (b) how sentiment varies with party ideology and incumbency status, and (c) how positive and negative sentiment relate to sharing overall and at the party level. Demonstrating an approach using large language models in combination with human annotation for sentiment analysis, the study finds that while positive sentiment remains dominant, negative sentiment has increased over time, especially among challenger and left-wing parties. Extremist parties on both the far-left and far-right post more negative content, but this does not consistently translate into higher sharing. Overall, negative posts tend to receive more shares, but this advantage appears to weaken over time, suggesting strategic adaptation and possible audience fatigue.
Perceptions of how algorithms curate content are integral to how people use social media and its effects. Building on the algorithm responsiveness process, we explore whether perceived algorithm responsiveness (PAR) was associated with the message cues of algorithmically curated content. We coupled longitudinal data from a 7-day, twice-daily diary survey with computational digital traces from Twitter (now X) to operationalize algorithmic message characteristics and to understand the antecedents and outcomes of PAR ( N = 10,337 tweets). Results suggested that message cues from algorithmic content in social feeds (i.e., closeness, popularity, valence, and format) were associated with perceived message personalization, but message personalization was the only message characteristic associated with PAR. PAR predicted feeling more connected to others on Twitter, along with message-level factors: closeness to the poster and message personalization. This work contributes to the algorithm responsiveness process, demonstrating that characteristics of algorithmically curated messages predict how individuals experience algorithms.
How much do experts on the information environment agree about the scale, spread, and effects of misinformation? We drew on three annual waves of the International Panel on the Information Environment expert survey to examine agreement with seven popular claims about misinformation (2023: N = 262; 2024: N = 413; 2025: N = 442; spanning 76 countries). Agreement varied widely across statements and was stable across waves. About 60% of experts agreed that falsehoods spread faster than the truth and that misinformation is widespread. About half agreed that misinformation has a substantial influence on behavior and that people readily believe what they see online; about 40% agreed that more people have become misinformed over time; and only 25% agreed that misinformation is primarily a social media problem. The surveys also revealed a persistent divide between experts who saw misinformation as widespread and consequential and those more skeptical, with sharpest disagreement on its behavioral impact and increase over time. In 2025, some experts distinguished between misperception prevalence and misinformation severity. Overall agreement varied by respondent background: agreement was higher among experts on developing countries than those on developed countries, higher among STEM researchers than social scientists, and lower among political scientists than communication scholars. Expert consensus on misinformation is partial, varies across claims and experts, and is shaped by geography and discipline.
Social media platforms structure the ways in which billions of citizens access information. Nevertheless, to assess social media time use, most research relies on limited data sources, such as self-reported survey measures or behavioral data from single platforms or convenience samples. Using 6 weeks of passive metering of smartphone and computer activity from a quota sample of Italian adults (18–70, n = 1,457), this study provides a behaviorally measured account of platform-specific social media use across the adult population. We quantify time allocation across Facebook, Instagram, TikTok, YouTube, and Twitter/X and examine how use varies by sociodemographic factors. Results show that Facebook and YouTube dominate everyday social media time, accounting for nearly three-quarters of consumption, while Instagram attracts comparable average time from a narrower user base, and TikTok shows concentrated usage among younger adopters. Twitter/X occupies a marginal niche. Use is overwhelmingly app-based (>90%), a pattern that browser-based measurement cannot capture. Age is the strongest demographic predictor: Gen Z concentrates attention on Instagram and TikTok, while older cohorts favor Facebook; YouTube shows weak demographic patterning. Gender and education are consistent secondary predictors: women devote more time to Facebook, Instagram, and social media overall, and higher education is associated with lower aggregate use and a smaller share on Facebook and TikTok. Employment and income show null effects. Together, these behavioral, cross-platform figures provide a more accurate basis for weighting platforms by their actual audiences than self-reported or single-platform evidence allows.
Social bots are an increasingly prominent presence in online protest discourse. We adapt the discursive opportunity structure (DOS) framework for digital protest, refining its dimensions of visibility, resonance and legitimacy to account for platform-mediated discourse and automated activity. Applying the refined framework through computer-assisted content analysis of bot activity in two major protests – Black Lives Matter (BLM) and Extinction Rebellion (XR) – we move beyond treating bots as a monolithic or uniformly disruptive force, identifying competing pro- and counter-protest strategies distributed differently across the three dimensions. In BLM, pro-protest bot content concentrates on cultural resonance, whereas counter-bot content targets legitimacy; in XR, pro-protest bots are used to build legitimacy and participatory visibility, whereas counter-bots more often work to downplay the movement’s salience. The framework’s granularity discriminates between movement types in ways coarser accounts of ‘political bots’ cannot, showing that understanding contemporary movements requires attending to how automated activity is deployed within their discourse.
