Social media platforms allow users to create polls to gather public opinion on diverse topics. However, we know little about what such polls are used for and how reliable they are, especially in significant contexts like elections. Focusing on the 2020 presidential elections in the U.S., this study shows that outcomes of election polls on Twitter deviate from election results despite their prevalence. Leveraging demographic inference and statistical analysis, we find that Twitter polls are disproportionately authored by male Republicans and exhibit a large bias towards candidate Donald Trump in comparison to mainstream polls. We investigate potential sources of biased outcomes from the point of view of inauthentic, automated, and counter-normative behavior. Using social media experiments and interviews with poll authors, we identify inconsistencies between public vote counts and those privately visible to poll authors, with the gap potentially attributable to purchased votes. We find that election polls tend to be more biased, contain more questionable votes, and attract more bots before the election day than after. We highlight and compare key factors contributing to biased poll outcomes. Finally, we identify instances of polls spreading voter fraud conspiracy theories and estimate that a couple of thousand such polls were posted in 2020. The study discusses the implications of biased election polls in the context of transparency and accountability of social media platforms.

The proliferation of misleading visualizations online, particularly during critical events like public health crises and elections, poses a significant risk. This study investigates the capability of GPT-4 models (4V, 4o, and 4o mini) to detect misleading visualizations. Utilizing a dataset of tweet-visualization pairs containing various visual misleaders, we test these models under four experimental conditions with different levels of guidance. We show that GPT-4 models can detect misleading visualizations with moderate accuracy without prior training (naive zero-shot) and that performance notably improves when provided with definitions of misleaders (guided zero-shot). However, a single prompt engineering technique does not yield the best results for all misleader types. Specifically, providing the models with misleader definitions and examples (guided few-shot) proves more effective for reasoning misleaders, while guided zero-shot performs better for design misleaders. This study underscores the feasibility of using large vision-language models to detect visual misinformation and the importance of prompt engineering for optimized detection accuracy.

Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.

Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so that model performance on unseen datasets is also optimized, in addition to traditional cross-validation. We find that strategically selecting a subset of training data yields better model accuracy and generalization than exhaustively training on all available data. Thanks to the simplicity of the proposed model, its logic can be interpreted to provide insights into social bot characteristics.

Malicious actors create inauthentic social media accounts controlled in part by algorithms, known as social bots, to disseminate misinformation and agitate online discussion. While researchers have developed sophisticated methods to detect abuse, novel bots with diverse behaviors evade detection. We show that different types of bots are characterized by different behavioral features. As a result, supervised learning techniques suffer severe performance deterioration when attempting to detect behaviors not observed in the training data. Moreover, tuning these models to recognize novel bots requires retraining with a significant amount of new annotations, which are expensive to obtain. To address these issues, we propose a new supervised learning method that trains classifiers specialized for each class of bots and combines their decisions through the maximum rule. The ensemble of specialized classifiers (ESC) can better generalize, leading to an average improvement of 56% in F1 score for unseen accounts across datasets. Furthermore, novel bot behaviors are learned with fewer labeled examples during retraining. We deployed ESC in the newest version of Botometer, a popular tool to detect social bots in the wild, with a cross-validation AUC of 0.99.

As cloud computing services have gained popularity, users view videos on websites (e.g., YouTube) to generate high CPU resource utilization and bandwidth for video streaming data centers. However, popular videos result in power-law features to cause imbalanced resource utilization. In addition, hotspot and idle servers generate extra power consumption in data centers. Previous studies considered to satisfy the requirements of users, provide faster access rates and save power consumption. However, fewer studies considered resource utilization with different popularity videos. Therefore, this paper proposes an energy efficient virtual machine (VM) management scheme with power-law features (VMPL). VMPL predicts the resource utilization of the video in the future based on the popularity, ensures enough resources for upcoming videos, and turns off idle servers for power saving. Simulation results validated by mathematical analysis show that VMPL has the best resource utilization and the lowest power consumption compared with Nash and Best-Fit algorithms.

We present a system which automatically reduces the ringtone volume as soon as users start picking up their mobile devices to answer incoming phone calls. Our system uses multiple sensors already in mobile phones (e.g. accelerometer, gyroscope, and proximity sensor) to extract 47 features, and uses supported vector machine (SVM) as the classifier to identify the act of picking up a phone. We have collected data under several typical conditions: including walking vs stationary and picking up the phone from inside a bag vs pockets. Our results show that the system can correctly identify users picking up their mobile phones 95% of the time on average.