United States

The environment we are in can affect our mood and behavior. One environment factor is weather, which is linked to sentiment as expressed on social media. However, less is known about how integrating changes in weather, along with time and location contextual cues, can improve sentiment detection and understanding. In this paper, we explore the effects of three contextual features--weather, location, and time--on expressed sentiment in social media. Leveraging a large Snapchat dataset, we provide extensive experimental evidence that including contextual features in addition to textual features significantly improves textual sentiment detection performance by 3\% over transformer-based language models. Our results also generalize cross-domain to Twitter. Ablation studies indicate the relative importance of weather compared to location and time. We also conduct correlation analyses on 8 million Snapchat posts to highlight the link between past weather and current sentiment, showing that weather has a lasting impact on mood.  Users generally exhibit more positive sentiment in better weather conditions as well as in improved weather conditions. Additionally, we show that temperature&#39;s link with mood holds after controlling for time or population density, but there exist geographical differences in how temperature affects mood. Our work demonstrates the effectiveness of including external contexts in linguistic tasks and carries design implications for researchers and designers of social media.

ICWSM 2022

Sunshine with a Chance of Smiles: How does Weather Impact Sentiment on Social Media?

weather

affect

mood

context

social media

sentiment

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

The environment we are in can affect our mood and behavior. One environment factor is weather, which is linked to sentiment as expressed on social media. However, less is known about how integrating changes in weather, along with time and location contextual cues, can improve sentiment detection and understanding. In this paper, we explore the effects of three contextual features--weather, location, and time--on expressed sentiment in social media. Leveraging a large Snapchat dataset, we provide extensive experimental evidence that including contextual features in addition to textual features significantly improves textual sentiment detection performance by 3\% over transformer-based language models. Our results also generalize cross-domain to Twitter. Ablation studies indicate the relative importance of weather compared to location and time. We also conduct correlation analyses on 8 million Snapchat posts to highlight the link between past weather and current sentiment, showing that weather has a lasting impact on mood.  Users generally exhibit more positive sentiment in better weather conditions as well as in improved weather conditions. Additionally, we show that temperature's link with mood holds after controlling for time or population density, but there exist geographical differences in how temperature affects mood. Our work demonstrates the effectiveness of including external contexts in linguistic tasks and carries design implications for researchers and designers of social media.

technical paper

**ICWSM-2022 will be held from June 6th – 9th in a hybrid format, both in-person in Atlanta, Georgia and online.**

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The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.


ICWSM, now in its sixteenth year, has become one of the premier venues for computational social science, and previous years of ICWSM have featured papers, posters, and demos that draw upon network science, machine learning, computational linguistics, sociology, communication, and political science. The uniqueness of the venue and the quality of submissions have contributed to a rapidly growing conference, and a competitive acceptance rate of approximately 20% for full-length research papers published in the proceedings by the Association for the Advancement of Artificial Intelligence (AAAI).


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The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.

On Reddit, individual subreddits are used to organize content and connect users. One mode of interaction is the subreddit link, which occurs when a user makes a direct reference to a subreddit in another community. Based on the ubiquity of these references, we have undertaken a study on subreddit links on Reddit, with the goal of understanding their impact on both the referenced subreddit, and on the subreddit landscape as a whole. By way of an extensive observational study along with several natural experiments using the entire history of Reddit, we were able to determine that (1) subreddit links are a significant driver of new suberddit creation; (2) subreddit links (2a) substantially drive activity in the referenced subreddit, and (2b) are frequently created in response to high levels of activity in the referenced subreddit; and (3) the graph of subreddit links has become less dense and more treelike over time. We conclude with a discussion of how these results confirm, add to, and in some cases conflict with existing theories on information-seeking behavior and self-organizing behavior in online social systems.

Subreddit Links Drive Community Creation and User Engagement on Reddit

Sinophobia, anti-Chinese sentiment, has existed on the Web for a long time. The outbreak of COVID-19 and the extended quarantine has further amplified it. However, we lack a quantitative understanding of the cause of Sinophobia as well as how it evolves over time. In this paper, we conduct a largescale longitudinal measurement of Sinophobia, between 2016 and 2021, on two mainstream and fringe Web communities. By analyzing 8B posts from Reddit and 206M posts from 4chan’s /pol/, we investigate the origins, evolution, and content of Sinophobia. We find that, anti-Chinese content can be evoked by political events not direclty related to China, e.g., the U.S. withdrawal from the Paris Agreement [59]. And during the COVID-19 pandemic, daily usage of Sinophobic slurs has significantly increased even with the hate-speech ban policy [43]. We also show that the semantic meaning of the words “China” and “Chinese” are shifting towards Sinophobic slurs with the rise of COVID-19 and remain the same in the pandemic period. We further use topic modeling to show the topics of Sinophobic discussion are pretty diverse and broad. We find that both Web communities share some common Sinophobic topics like ethnics, economics and commerce, weapons and military, foreign relations, etc. However, compared to 4chan’s /pol/, more daily life-related topics including food, game, and stock are found in Reddit. Our finding also reveals that the topics related to COVID-19 and blaming the Chinese government are more prevalent in the pandemic period. To the best of our knowledge, this paper is the longest quantitative measurement of Sinophobia.

On Xing Tian and the Perseverance of Anti-China Sentiment Online

The proliferation of harmful and offensive content is a problem that many online platforms face today. One of the most common approaches for moderating offensive content online is via the identification and removal after it has been posted, increasingly assisted by machine learning algorithms. More recently, platforms have begun employing moderation approaches which seek to intervene prior to offensive content being posted. In this paper, we conduct an online randomized controlled experiment on Twitter to evaluate a new intervention that aims to encourage participants to reconsider their offensive content and, ultimately, seeks to reduce the amount of offensive content on the platform. The intervention prompts users who are about to post harmful content with an opportunity to pause and reconsider their Tweet. We find that users in our treatment prompted with this intervention posted 6% fewer offensive Tweets than non-prompted users in our control. This decrease in the creation of offensive content can be attributed not just to the deletion and revision of prompted Tweets - we also observed a decrease in both the number of offensive Tweets that prompted users create in the future and the number of offensive replies to prompted Tweets. We conclude that interventions allowing users to reconsider their comments can be an effective mechanism for reducing offensive content online.

Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content

Online social support via avatar communication is a powerful tool for bullying victims because they often lack offline social resources. Additionally, avatar communication allows users rich nonverbal interactions (e.g., emotional expressions) while maintaining online anonymity. This study investigates the role of online social support via avatars for victims and how to facilitate such support. Accordingly, we conducted an online questionnaire survey twice on an avatar communication application, Pigg Party, regarding mental health, offline and online social support, and offline bullying victimization (participants: 3,288 (1st wave) and 758 (2nd wave)). We found that online social support via avatars supplemented insufficient offline social resources, particularly when there was a high risk of offline bullying victimization. Furthermore, we investigated how online social support is improved by ego networks using social network data from Pigg Party. We demonstrated that belonging to large and closely connected communities can enhance online social support. Our findings suggest that avatar communication applications can improve players' mental health through online social support, reinforced by facilitating ego networks.

Online Social Support via Avatar Communication Buffers Harmful Effects of Offline Bullying Victimization

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population --- a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction.  Across four tasks of predicting U.S. county population health statistics from Twitter, we find standard restratification techniques provide no improvement and often degrade prediction accuracies. The core reasons for this seems to be both shrunken estimates (reduced variance of model predicted values) and sparse estimates of each population's socio-demographics. We thus develop and evaluate three methods to address these problems: estimator redistribution to account for shrinking, and adaptive binning and informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods significantly outperforms the standard restratification approaches. Combining approaches, we find substantial improvements over non-restratified models, yielding a 53.0% increase in predictive accuracy (R^2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks.

Correcting Sociodemographic Selection Biases for Population Prediction from Social Media

Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance. Audits of these applications have revealed that they tend to be biased against minority groups which result in unfair and concerning societal and political outcomes. Despite multiple studies over time, these biases have not been mitigated completely and have in fact increased for certain tasks like age prediction. While such systems are audited over benchmark datasets, it becomes necessary to evaluate their robustness for adversarial inputs. In this work, we perform an extensive adversarial audit on multiple systems and datasets, making a number of concerning observations- there has been a drop in accuracy for some tasks on CelebSET dataset since a previous audit. While there still exists a bias in accuracy against individuals from minority groups for multiple datasets, a more worrying observation is that these biases tend to get exorbitantly pronounced with adversarial inputs toward the minority group. We conclude with a discussion on the broader societal impacts in light of these observations and a few suggestions on how to collectively deal with this issue.

Two-Face: Adversarial Audit of Commercial Face Recognition Systems

Much attention has been on the behaviors of computer vision services when describing images of people. Audits have revealed rampant biases that could lead to harm, when services are used by developers and researchers. We focus on temporal auditing, replicating experiments originally conducted three years ago. We document the changes observed over time, relating this to the growing awareness of structural oppression and the need to align technology with social values. While we document some positive changes in the services’ behaviors, such as increased accuracy in the use of gender-related tags overall, we also replicate findings concerning larger error rates for images of Black individuals. In addition, we find cases of increased use of inferential tags (e.g., emotions), which are often sensitive. The analysis underscores the difficulty in following changes in services’ behaviors over time, and the need for more oversight of such services.

Shifting our Awareness, Taking Back Tags: Temporal Changes in Computer Vision Services' Social Behaviors

Link prediction has been widely applied in social network analysis. Despite its importance, link prediction algorithms can be biased by disfavoring the links between individuals in particular demographic groups. In this paper, we study one particular type of bias, namely, the bias in predicting inter-group links (i.e., links across different demographic groups). First, we formalize the definition of bias in link prediction by providing quantitative measurements of accuracy disparity, which measures the difference in prediction accuracy of inter-group and intra-group links. Second, we unveil the existence of bias in six existing state-of-the-art link prediction algorithms through extensive empirical studies over real world datasets. Third, we identify the imbalanced density across intra-group and inter-group links in training graphs as one of the underlying causes of bias in link prediction. Based on the identified cause, fourth, we design a pre-processing bias mitigation method named FairLP to modify the training graph, aiming to balance the distribution of intra-group and inter-group links while preserving the network characteristics of the graph. FairLP is model-agnostic and thus is compatible with any existing link prediction algorithm. Our experimental results on real-world social network graphs demonstrate that FairLP achieves better trade-off between fairness and prediction accuracy than the existing fairness-enhancing link prediction methods.

FairLP: Towards Fair Link Prediction on Social Network Graphs

In this work we study the association between the song lyrics and \textit{mood} associations with the song, and conduct a data driven analysis to compare how lyrics and acoustics each contribute to the understanding of a song's mood. We conduct our study on a data set containing nearly one million songs, with song-mood associations derived from user playlists on a streaming platform. We take advantage of  state-of-the-art natural language processing models based on transformers to learn the association between the lyrics and moods. We find that a pretrained transformer-based model in zero-shot setting -- i.e., out of the box with no further training on our data -- is powerful for capturing song-mood associations. Moreover, we illustrate that training on song-mood associations results in a highly accurate model that predicts these associations for unseen songs. Furthermore, by comparing the prediction of a model using lyrics with one using acoustics, we observe that the relative importance of lyrics in comparison with acoustics depends on the specific mood. Finally, we verify if the models are capturing the same information about lyrics and acoustics as humans by conducting a manual annotation task on a hundred songs where we obtain human judgments of mood-song relevance based on lyrics and acoustics in isolation.

The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood

Online review systems are the primary means through which many businesses seek to build the brand and spread their messages. Prior research studying the effects of online reviews has been mainly focused on a single numerical cause, e.g., ratings or sentiment scores. We argue that such notions of causes entail three key limitations: they solely consider the effects of single numerical causes and ignore different effects of multiple aspects -- e.g., Food, Service -- embedded in the textual reviews; they assume away the presence of hidden confounders in observational studies, e.g., consumers' personal preferences; and they overlook the indirect effects of numerical causes that can potentially cancel out the effect of textual reviews on business revenue. We thereby propose an alternative perspective to this single-cause-based effect estimation of online reviews: in the presence of hidden confounders, we consider multi-aspect textual reviews, particularly, their total effects on business revenue and direct effects with the numerical cause -- ratings -- being the mediator. We draw on recent advances in machine learning and causal inference to together estimate the hidden confounders and causal effects. We present empirical evaluations using real-world examples to discuss the importance and implications of differentiating the multi-aspect effects in strategizing business operations.

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