People are increasingly able to receive customized options. Despite this abundance of options, people may not view products as customized to their wants and needs. Across five experiments, we provide evidence for a possible solution. We find evidence for the Illusion of increased customization: Framing choices as a creative process increases a chosen option’s perceived customization. Even with a constant choice set, choosing by (creative) attributes rather than choosing from all available options produces the Illusion of increased customization. The Illusion of increased customization arises in part because people feel a greater sense of co-creation when choosing via a seemingly creative process. Consequently, the Illusion of increased customization extends to choices that express preferences (e.g., liking blue over red) but is significantly diminished with choices that describe objective needs (e.g., needing a small versus large T-shirt). Additionally, heightened perceived customization from the Illusion of increased customization results in greater willingness-to-pay for the chosen option.

Learning general-purpose user representations based on user behavioral logs is an increasingly popular user modeling approach. It benefits from easily available, privacy-friendly yet expressive data, and does not require extensive re-tuning of the upstream user model for different downstream tasks. While this approach has shown promise in search engines and e-commerce applications, its fit for instant messaging platforms, a cornerstone of modern digital communication, remains largely uncharted. We explore this research gap using Snapchat data as a case study. Specifically, we implement a Transformer-based user model with customized training objectives and show that the model can produce high-quality user representations across a broad range of evaluation tasks, among which we introduce three new downstream tasks that concern pivotal topics in user research: user safety, engagement and churn. We also tackle the challenge of efficient extrapolation of long sequences at inference time, by applying a novel positional encoding method.

Pictorial emojis and stickers are commonly used in online social communications. We analyzed social communications using Bitmoji stickers, which are expressive pictorial stickers made from avatars resembling actual users. We collect a large-scale dataset of 3 billion Bitmoji stickers’ metadata, shared among 300 million Snapchat users. We find that individual Bitmoji sticker usage patterns can be characterized jointly on dimensions of reciprocity and selectivity. Generally speaking, users are either both reciprocal and selective about whom they use Bitmoji stickers with or neither reciprocal nor selective. We additionally demonstrate network homophily by showing that friends use Bitmoji stickers at similar rates. Finally, using a quasi-experimental approach, we show that receiving Bitmoji stickers from a friend encourages future Bitmoji sticker usage and overall Snapchat engagement. Our work carries implications for a better understanding of online pictorial communication behaviors.

Culture shapes people’s behavior, both online and offline. Surprisingly, there is sparse research on how cultural context affects network formation and content consumption on social media. We analyzed the friendship networks and dyadic relations between content producers and consumers across 73 countries through a cultural lens in a closed-network setting. Closed networks allow for intimate bonds and self-expression, providing a natural setting to study cultural differences in behavior. We studied three theoretical frameworks of culture - individualism, relational mobility, and tightness. We found that friendship networks formed across different cultures differ in egocentricity, meaning the connectedness between a user’s friends. Individualism, mobility, and looseness also significantly negatively impact how tie strength affects content consumption. Our findings show how culture affects social media behavior, and we outline how researchers can incorporate this in their work. Our work has implications for content recommendations and can improve content engagement.

Understanding the users' patterns of visiting various location categories can help online platforms improve content personalization and user experiences. Current literature on predicting future location categories of a user typically employs features that can be traced back to the user, such as spatial geo-coordinates and demographic identities. Moreover, existing approaches commonly suffer from cold-start and generalization problems, and often cannot specify when the user will visit the predicted location category. In a large social platform, it is desirable for prediction models to avoid using user-identifiable data, generalize to unseen and new users, and be able to make predictions for specific times in the future. In this work, we construct a neural model, LocHabits, using data from Snapchat. The model omits user-identifiable inputs, leverages temporal and sequential regularities in the location category histories of Snapchat users and their friends, and predicts the users' next-hour location categories. We evaluate our model on several real-life, large-scale datasets from Snapchat and FourSquare, and find that the model can outperform baselines by 14.94% accuracy. We confirm that the model can (1) generalize to unseen users from different areas and times, and (2) fall back on collective trends in the cold-start scenario. We also study the relative contributions of various factors in making the predictions and find that the users' visitation preferences and most-recent visitation sequences play more important roles than time contexts, same-hour sequences, and social influence features.

Showing ads delivers revenue for online content distributors, but ad exposure can compromise user experience and cause user fatigue and frustration. Correctly balancing ads with other content is imperative. Currently, ad allocation relies primarily on demographics and inferred user interests, which are treated as static features and can be privacy-intrusive. This paper uses person-centric and momentary context features to understand optimal ad-timing. In a quasi-experimental study on a three-month longitudinal dataset of 100K Snapchat users, we find ad timing influences ad effectiveness. We draw insights on the relationship between ad effectiveness and momentary behaviors such as duration, interactivity, and interaction diversity. We simulate ad reallocation, finding that our study-driven insights lead to greater value for the platform. This work advances our understanding of ad consumption and bears implications for designing responsible ad allocation systems, improving both user and platform outcomes. We discuss privacy-preserving components and ethical implications of our work.

How do users on social platforms consume content shared by their friends? Is this consumption socially motivated, and can we predict it? Considerable prior work has focused on inferring and learning user preferences with respect to broadcasted, or open-network content in public spheres like webpages or public videos. However, user engagement with narrowcasted, closed-network content shared by their friends is considerably under-explored, despite being a commonplace activity. Here we bridge this gap by focusing on consumption of visual media content in closed-network settings, using data from Snapchat, a large multimedia-driven social sharing service with over 200M daily active users. Broadly, we answer questions around content consumption patterns, social factors that are associated with such consumption habits, and predictability of consumption time. We propose models for patterns in users' time-spending behaviors across friends, and observe that viewers preferentially and consistently spend more time on content from certain friends, even without considering any explicit notion of intrinsic content value. We also find that consumption time is highly correlated with several engagement-based social factors, suggesting a large social role in closed-network content consumption. Finally, we propose a novel approach of modeling future consumption time as a learning-to-rank task over users? friends. Our results demonstrate significant predictive value (0.815 [email protected], 0.650 [email protected]) using only social factors. We expect our work to motivate additional research in modeling consumption and ranking of online closed-network content.

Social media platforms support the sharing of written text, video, and audio. All of these formats may be inaccessible to people who are deaf or hard of hearing (DHH), particularly those who primarily communicate via sign language, people who we call Deaf signers. We study how Deaf signers engage with social platforms, focusing on how they share content and the barriers they face. We employ a mixed-methods approach involving seven in-depth interviews and a survey of a larger population (n = 60). We find that Deaf signers share the most in written English, despite their desire to share in sign language. We further identify key areas of difficulty in consuming content (e.g., lack of captions for spoken content in videos) and producing content (e.g., captioning signed videos, signing into a phone camera) on social media platforms. Our results both provide novel insights into social media use by Deaf signers and reinforce prior findings on DHH communication more generally, while revealing potential ways to make social media platforms more accessible to Deaf signers.

Psychological studies have shown that personality traits are associated with book preferences. However, past findings based on questionnaires are limited to conventional book genres and do not capture niche content (e.g., family drama) and reading behaviors (e.g., backburners). For a more comprehensive measure of book content, this study harnesses a massive archive of content labels, also known as `tags', created by users of a book review website, Goodreads.com. Combined with data on preferences and personality scores collected from Facebook users, the tag labels achieve high accuracy in personality prediction by psychological standards. Additionally, we group tags into broader genres to check their validity against past findings. Our results are robust across both tag-level and genre-level analyses and are consistent with existing literature. Moreover, user-generated tag labels reveal unexpected insights, such as cultural differences, book reading behaviors, and other non-content factors affecting preferences. To our knowledge, this is currently the largest study that explores the relationship between personality and book content preferences.

Camera glasses enable people to capture point-of-view videos using a common accessory, hands-free. In this paper, we investigate how, when, and why people used one such product: Spectacles. We conducted 39 semi-structured interviews and surveys with 191 owners of Spectacles. We found that the form factor elicits sustained usage behaviors, and opens opportunities for new use-cases and types of content captured. We provide a usage typology, and highlight societal and individual factors that influence the classification of behaviors.