How Constraints Affect Content: The Case of Twitter's Switch from 140 to 280 Characters

It is often said that constraints affect creative production, both in terms of form and quality. Online social media platforms frequently impose constraints on the content that users can produce, limiting the range of possible contributions. Do these restrictions tend to push creators towards producing more or less successful content? How do creators adapt their contributions to fit the limits imposed by social media platforms? To answer these questions, we conduct an observational study of a recent event: on November 7, 2017, Twitter changed the maximum allowable length of a tweet from 140 to 280 characters, thereby significantly altering its signature constraint. In the first study of this switch, we compare tweets with nearly or exactly 140 characters before the change to tweets of the same length posted after the change. This setup enables us to characterize how users alter their tweets to fit the constraint and how this affects their tweets' success. We find that in response to a length constraint, users write more tersely, use more abbreviations and contracted forms, and use fewer definite articles. Also, although in general tweet success increases with length, we find initial evidence that tweets made to fit the 140-character constraint tend to be more successful than similar-length tweets written when the constraint was removed, suggesting that the length constraint improved tweet quality.Comment: To appear in the Proceedings of AAAI ICWSM 201

Population-scale dietary interests during the COVID-19 pandemic.

The SARS-CoV-2 virus has altered people's lives around the world. Here we document population-wide shifts in dietary interests in 18 countries in 2020, as revealed through time series of Google search volumes. We find that during the first wave of the COVID-19 pandemic there was an overall surge in food interest, larger and longer-lasting than the surge during typical end-of-year holidays in Western countries. The shock of decreased mobility manifested as a drastic increase in interest in consuming food at home and a corresponding decrease in consuming food outside of home. The largest (up to threefold) increases occurred for calorie-dense carbohydrate-based foods such as pastries, bakery products, bread, and pies. The observed shifts in dietary interests have the potential to globally affect food consumption and health outcomes. These findings can inform governmental and organizational decisions regarding measures to mitigate the effects of the COVID-19 pandemic on diet and nutrition

Grounding or Guesswork? Large Language Models are Presumptive Grounders

Effective conversation requires common ground: a shared understanding between the participants. Common ground, however, does not emerge spontaneously in conversation. Speakers and listeners work together to both identify and construct a shared basis while avoiding misunderstanding. To accomplish grounding, humans rely on a range of dialogue acts, like clarification (What do you mean?) and acknowledgment (I understand.). In domains like teaching and emotional support, carefully constructing grounding prevents misunderstanding. However, it is unclear whether large language models (LLMs) leverage these dialogue acts in constructing common ground. To this end, we curate a set of grounding acts and propose corresponding metrics that quantify attempted grounding. We study whether LLMs use these grounding acts, simulating them taking turns from several dialogue datasets, and comparing the results to humans. We find that current LLMs are presumptive grounders, biased towards assuming common ground without using grounding acts. To understand the roots of this behavior, we examine the role of instruction tuning and reinforcement learning with human feedback (RLHF), finding that RLHF leads to less grounding. Altogether, our work highlights the need for more research investigating grounding in human-AI interaction.Comment: 16 pages, 2 figure

Sudden Attention Shifts on Wikipedia During the COVID-19 Crisis

We study how the COVID-19 pandemic, alongside the severe mobility restrictions that ensued, has impacted information access on Wikipedia, the world's largest online encyclopedia. A longitudinal analysis that combines pageview statistics for 12 Wikipedia language editions with mobility reports published by Apple and Google reveals massive shifts in the volume and nature of information seeking patterns during the pandemic. Interestingly, while we observe a transient increase in Wikipedia's pageview volume following mobility restrictions, the nature of information sought was impacted more permanently. These changes are most pronounced for language editions associated with countries where the most severe mobility restrictions were implemented. We also find that articles belonging to different topics behaved differently; e.g., attention towards entertainment-related topics is lingering and even increasing, while the interest in health- and biology-related topics was either small or transient. Our results highlight the utility of Wikipedia for studying how the pandemic is affecting people's needs, interests, and concerns.Comment: Manoel Horta Ribeiro, Kristina Gligori\'c and Maxime Peyrard contributed equally to this work. Also, this paper has been accepted at the 15th International Conference on Web and Social Media (ICWSM), please cite accordingl

On the context-free ambiguity of emoji

Due to their pictographic nature, emojis come with baked-in, grounded semantics. Although this makes emojis promising candidates for new forms of more accessible communication, it is still unknown to what degree humans agree on the inherent meaning of emojis when encountering them outside of concrete textual contexts. To bridge this gap, we collected a crowdsourced dataset (made publicly available) of one-word descriptions for 1,289 emojis presented to participants with no surrounding text. The emojis and their interpretations were then examined for ambiguity. We find that, with 30 annotations per emoji, 16 emojis (1.2%) are completely unambiguous, whereas 55 emojis (4.3%) are so ambiguous that the variation in their descriptions is as high as that in randomly chosen descriptions. Most emojis lie between these two extremes. Furthermore, investigating the ambiguity of different types of emojis, we find that emojis representing symbols from established, yet not cross-culturally familiar code books (e.g., zodiac signs, Chinese characters) are most ambiguous. We conclude by discussing design implications

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates