Modeling Twitter Engagement in Real-World Events

Twitter offers tremendous opportunities for people to engage with real-world events (e.g., political election) through information sharing and communicating about these events. However, little is understood about the factors that affect people’s Twitter engagement (e.g., posting) in such real-world events. This paper examines multiple predictive factors associated with four different perspectives of users’ Twitter engagement, and quantify their potential influence on predicting the (i) presence; and (ii) degree of the user’s engagement with real-world events. We find that the measures of people’s prior Twitter activities, topical interests, geolocation, and social network structures are all variously correlated to their engagement with real-world events.

Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram

Instagram is a relatively new form of communication where users can instantly share their current status by taking pictures and tweaking them using filters. It has seen a rapid growth in the number of users as well as uploads since it was launched in October 2010. Inspite of the fact that it is the most popular photo sharing application, it has attracted relatively less attention from the web and social media research community. In this paper, we present a large-scale quantitative analysis on millions of users and pictures we crawled over 1 month from Instagram. Our analysis reveals several insights on Instagram which were never studied before: 1) its social network properties are quite different from other popular social media like Twitter and Flickr, 2) people typically post once a week, and 3) people like to share their locations with friends. To the best of our knowledge, this is the first in-depth analysis of user activities, demographics, social network structure and user-generated content on Instagram.Comment: 5 page

Feature Learning in Image Hierarchies using Functional Maximal Correlation

This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in multiview systems. By framing view similarities as dependencies and ensuring contrastivity by imposing orthonormality, HFMCA achieves faster convergence and increased stability in self-supervised learning. HFMCA defines and measures dependencies within image hierarchies, from pixels and patches to full images. We find that the network topology for approximating orthonormal basis functions aligns with a vanilla CNN, enabling the decomposition of density ratios between neighboring layers of feature maps. This approach provides powerful interpretability, revealing the resemblance between supervision and self-supervision through the lens of internal representations

Relevance-based Retrieval on Hidden-Web Text Databases without Ranking Support

Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an Information Retrieval (IR) ranking function. The naive approach would be to submit a disjunctive query with all query keywords, retrieve the returned documents, and then re-rank them. Unfortunately, such an operation would be very expensive due to the large number of results returned by disjunctive queries. In this paper we present algorithms that return the top results for a query, ranked according to an IR-style ranking function, while operating on top of a source with a Boolean query interface with no ranking capabilities (or a ranking capability of no interest to the end user). The algorithms generate a series of conjunctive queries that return only documents that are candidates for being highly ranked according to a relevance metric. Our approach can also be applied to other settings where the ranking is monotonic on a set of factors (query keywords in IR) and the source query interface is a Boolean expression of these factors. Our comprehensive experimental evaluation on the PubMed database and a TREC dataset show that we achieve order of magnitude improvement compared to the current baseline approaches.Vagelis Hristidis was partly supported by NSF grant IIS-0811922 and DHS grant 2009-ST-062-000016. Panagiotis G.\ Ipeirotis was supported by the National Science Foundation under Grant No. IIS-0643846

Get a Word in Edgewise: Post Character Limit and Social Media-Based Customer Service

In this paper, we study the role of extending character limits on firm responses on social media. By leveraging a natural experiment setting: the unexpected increase in post character limit on Twitter, we empirically investigate the impact on the linguistic styles of social media-based customer service responses. Using a Regression Discontinuity in Time Design and leveraging a panel dataset, our results suggest that extending character limits influences firm to change the linguistic styles in their responses which could influence consumers' perceptions. Our results show that extending post-character limits significantly reduces the readability ease of firm responses, on average, while increasing the concreteness and personal closeness scores of these responses, on average. We show that these changes were effective in influencing customer satisfaction

Online Review Censorship

Ample anecdotal evidence in the media notes that many businesses seek to ‘silence’ negative reviews, e.g., via legal threat. Despite attention toward this issue, we are aware of no systematic analyses addressing it. We address that gap here, leveraging review data from TripAdvisor.com. First, we estimate that ~1% of truthful reviews are deleted within six months of posting and that negative reviews are significantly more likely to be deleted, consistent with a mechanism of censorship. The effect is substantial; we estimate that a 1-star decrease in rating valence is associated with an approximate 25% (0.25pp) increase in the probability of deletion. Second, we examine how freedom of expression (FoE) in a country associates with characteristics of (uncensored) online reviews. We find that FoE associates with larger review volumes, lower review valence, and faster review posting. We discuss implications for online ratings platforms, consumers, and research opportunities

Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT

In this paper, we aimed to provide a review and tutorial for researchers in the field of medical imaging using language models to improve their tasks at hand. We began by providing an overview of the history and concepts of language models, with a special focus on large language models. We then reviewed the current literature on how language models are being used to improve medical imaging, emphasizing different applications such as image captioning, report generation, report classification, finding extraction, visual question answering, interpretable diagnosis, and more for various modalities and organs. The ChatGPT was specially highlighted for researchers to explore more potential applications. We covered the potential benefits of accurate and efficient language models for medical imaging analysis, including improving clinical workflow efficiency, reducing diagnostic errors, and assisting healthcare professionals in providing timely and accurate diagnoses. Overall, our goal was to bridge the gap between language models and medical imaging and inspire new ideas and innovations in this exciting area of research. We hope that this review paper will serve as a useful resource for researchers in this field and encourage further exploration of the possibilities of language models in medical imaging

Graph Neural Network for Customer Engagement Prediction on Social Media Platforms

Social media platforms such as Twitter and Facebook play a pivotal role in companies’ strategy of engaging customers. How to target potential customers on social media effectively and efficiently is an important yet unsolved question. Predicting customer engagement on social media platforms is facing several challenges that cannot be solved by traditional methods. In this work, we design a framework that leverages individual behavior on Facebook together with network contextual information to predict customer engagement (like/comment/share) of a brand’s posts. We first build a meta-path based Heterogeneous Information Network (HIN) to exploit large-scale content consumption information. We then design a Graph Neural Network (GNN) model combined with attention mechanism to learn structural feature representations of users to make the customer-brand engagement prediction. The proposed model is examined using a large-scale Facebook dataset and the result shows significant performance improvement compared with state-of-the-art baselines. Besides, the effectiveness of attention mechanism reveals the potential interpretability of the proposed model for the prediction results