المؤلفون: Liu, Hou-I, Tseng, Yu-Wen, Chang, Kai-Cheng, Wang, Pin-Jyun, Shuai, Hong-Han, Cheng, Wen-Huang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Despite notable advancements in the field of computer vision, the precise detection of tiny objects continues to pose a significant challenge, largely owing to the minuscule pixel representation allocated to these objects in imagery data. This challenge resonates profoundly in the domain of geoscience and remote sensing, where high-fidelity detection of tiny objects can facilitate a myriad of applications ranging from urban planning to environmental monitoring. In this paper, we propose a new framework, namely, DeNoising FPN with Trans R-CNN (DNTR), to improve the performance of tiny object detection. DNTR consists of an easy plug-in design, DeNoising FPN (DN-FPN), and an effective Transformer-based detector, Trans R-CNN. Specifically, feature fusion in the feature pyramid network is important for detecting multiscale objects. However, noisy features may be produced during the fusion process since there is no regularization between the features of different scales. Therefore, we introduce a DN-FPN module that utilizes contrastive learning to suppress noise in each level's features in the top-down path of FPN. Second, based on the two-stage framework, we replace the obsolete R-CNN detector with a novel Trans R-CNN detector to focus on the representation of tiny objects with self-attention. Experimental results manifest that our DNTR outperforms the baselines by at least 17.4% in terms of APvt on the AI-TOD dataset and 9.6% in terms of AP on the VisDrone dataset, respectively. Our code will be available at https://github.com/hoiliu-0801/DNTRTest.
Comment: The article is accepted by IEEE Transactions on Geoscience and Remote Sensing. Our code will be available at https://github.com/hoiliu-0801/DNTRTest

الوصول الحر: http://arxiv.org/abs/2406.05755Test

المؤلفون: Scarano, Stephen, Vasudevan, Vijayalakshmi, Samory, Mattia, Yang, Kai-Cheng, Yang, JungHwan, Grabowicz, Przemyslaw A.

مصطلحات موضوعية: Computer Science - Social and Information Networks, Computer Science - Computers and Society, Physics - Physics and Society

الوصف: 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 older males and exhibit a large bias towards candidate Donald Trump relative to representative 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 also find that Twitter accounts participating in election polls are more likely to be bots, and election poll outcomes tend to be more biased, before the election day than after. Finally, we identify instances of polls spreading voter fraud conspiracy theories and estimate that a couple thousand of 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.
Comment: 14 pages, 10 figures

الوصول الحر: http://arxiv.org/abs/2405.11146Test

المؤلفون: Yang, Kai-Cheng, Singh, Danishjeet, Menczer, Filippo

مصطلحات موضوعية: Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks

الوصف: Recent advancements in generative artificial intelligence (AI) have raised concerns about their potential to create convincing fake social media accounts, but empirical evidence is lacking. In this paper, we present a systematic analysis of Twitter (X) accounts using human faces generated by Generative Adversarial Networks (GANs) for their profile pictures. We present a dataset of 1,420 such accounts and show that they are used to spread scams, spam, and amplify coordinated messages, among other inauthentic activities. Leveraging a feature of GAN-generated faces -- consistent eye placement -- and supplementing it with human annotation, we devise an effective method for identifying GAN-generated profiles in the wild. Applying this method to a random sample of active Twitter users, we estimate a lower bound for the prevalence of profiles using GAN-generated faces between 0.021% and 0.044% -- around 10K daily active accounts. These findings underscore the emerging threats posed by multimodal generative AI. We release the source code of our detection method and the data we collect to facilitate further investigation. Additionally, we provide practical heuristics to assist social media users in recognizing such accounts.
Comment: 18 pages, 6 figures; added a new dataset and made some minor revisions

الوصول الحر: http://arxiv.org/abs/2401.02627Test

المؤلفون: Yang, Kai-Cheng, Varol, Onur, Nwala, Alexander C., Sayyadiharikandeh, Mohsen, Ferrara, Emilio, Flammini, Alessandro, Menczer, Filippo

مصطلحات موضوعية: Computer Science - Social and Information Networks

الوصف: While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter, we focus on malicious social bots, a prominent vehicle for such manipulation. We start by discussing recent studies about the presence and actions of social bots in various online discussions to show their real-world implications and the need for detection methods. Then we discuss the challenges of bot detection methods and use Botometer, a publicly available bot detection tool, as a case study to describe recent developments in this area. We close with a practical guide on how to handle social bots in social media research.
Comment: This is a draft of the chapter. The final version will be available in the Handbook of Computational Social Science edited by Taha Yasseri, forthcoming 2024, Edward Elgar Publishing Ltd. The material cannot be used for any other purpose without further permission of the publisher and is for private use only

الوصول الحر: http://arxiv.org/abs/2312.17423Test

المؤلفون: Duan, Zening, Shao, Anqi, Hu, Yicheng, Lee, Heysung, Liao, Xining, Suh, Yoo Ji, Kim, Jisoo, Yang, Kai-Cheng, Chen, Kaiping, Yang, Sijia

مصطلحات موضوعية: Computer Science - Computation and Language

الوصف: While researchers often study message features like moral content in text, such as party manifestos and social media, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dictionary-based methods are cost-effective and computationally efficient, they often lack contextual sensitivity and are limited by the vocabularies developed for the original applications. In this paper, we present an approach to construct vec-tionary measurement tools that boost validated dictionaries with word embeddings through nonlinear optimization. By harnessing semantic relationships encoded by embeddings, vec-tionaries improve the measurement of message features from text, especially those in short format, by expanding the applicability of original vocabularies to other contexts. Importantly, a vec-tionary can produce additional metrics to capture the valence and ambivalence of a message feature beyond its strength in texts. Using moral content in tweets as a case study, we illustrate the steps to construct the moral foundations vec-tionary, showcasing its ability to process texts missed by conventional dictionaries and word embedding methods and to produce measurements better aligned with crowdsourced human assessments. Furthermore, additional metrics from the vec-tionary unveiled unique insights that facilitated predicting outcomes such as message retransmission.

الوصول الحر: http://arxiv.org/abs/2312.05990Test

المؤلفون: DeVerna, Matthew R., Yan, Harry Yaojun, Yang, Kai-Cheng, Menczer, Filippo

مصطلحات موضوعية: Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Computers and Society

الوصف: Fact checking can be an effective strategy against misinformation, but its implementation at scale is impeded by the overwhelming volume of information online. Recent artificial intelligence (AI) language models have shown impressive ability in fact-checking tasks, but how humans interact with fact-checking information provided by these models is unclear. Here, we investigate the impact of fact-checking information generated by a popular large language model (LLM) on belief in, and sharing intent of, political news in a preregistered randomized control experiment. Although the LLM performs reasonably well in debunking false headlines, we find that it does not significantly affect participants' ability to discern headline accuracy or share accurate news. Subsequent analysis reveals that the AI fact-checker is harmful in specific cases: it decreases beliefs in true headlines that it mislabels as false and increases beliefs in false headlines that it is unsure about. On the positive side, the AI fact-checking information increases sharing intents for correctly labeled true headlines. When participants are given the option to view LLM fact checks and choose to do so, they are significantly more likely to share both true and false news but only more likely to believe false news. Our findings highlight an important source of potential harm stemming from AI applications and underscore the critical need for policies to prevent or mitigate such unintended consequences.

الوصول الحر: http://arxiv.org/abs/2308.10800Test

المؤلفون: Yang, Kai-Cheng, Menczer, Filippo

المصدر: Journal of Quantitative Description: Digital Media (2024)

مصطلحات موضوعية: Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks

الوصف: Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far remains anecdotal. This paper presents a case study about a Twitter botnet that appears to employ ChatGPT to generate human-like content. Through heuristics, we identify 1,140 accounts and validate them via manual annotation. These accounts form a dense cluster of fake personas that exhibit similar behaviors, including posting machine-generated content and stolen images, and engage with each other through replies and retweets. ChatGPT-generated content promotes suspicious websites and spreads harmful comments. While the accounts in the AI botnet can be detected through their coordination patterns, current state-of-the-art LLM content classifiers fail to discriminate between them and human accounts in the wild. These findings highlight the threats posed by AI-enabled social bots.

الوصول الحر: http://arxiv.org/abs/2307.16336Test

المؤلفون: Yang, Kai-Cheng, Menczer, Filippo

مصطلحات موضوعية: Computer Science - Computation and Language

الوصف: Although large language models (LLMs) have shown exceptional performance in various natural language processing tasks, they are prone to hallucinations. State-of-the-art chatbots, such as the new Bing, attempt to mitigate this issue by gathering information directly from the internet to ground their answers. In this setting, the capacity to distinguish trustworthy sources is critical for providing appropriate accuracy contexts to users. Here we assess whether ChatGPT, a prominent LLM, can evaluate the credibility of news outlets. With appropriate instructions, ChatGPT can provide ratings for a diverse set of news outlets, including those in non-English languages and satirical sources, along with contextual explanations. Our results show that these ratings correlate with those from human experts (Spearmam's $\rho=0.54, p<0.001$). These findings suggest that LLMs could be an affordable reference for credibility ratings in fact-checking applications. Future LLMs should enhance their alignment with human expert judgments of source credibility to improve information accuracy.
Comment: 10 pages, 3 figures

الوصول الحر: http://arxiv.org/abs/2304.00228Test

المؤلفون: Cresci, Stefano, Yang, Kai-Cheng, Spognardi, Angelo, Di Pietro, Roberto, Menczer, Filippo, Petrocchi, Marinella

مصطلحات موضوعية: Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning

الوصف: Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for ambiguities, unrealistic expectations, and seemingly irreconcilable findings. Overcoming such issues is instrumental towards ensuring reliable solutions and reaffirming the validity of the scientific method. In this contribution, we review some recent results in social bots research, highlighting and revising factual errors as well as methodological and conceptual biases. More importantly, we demystify common misconceptions, addressing fundamental points on how social bots research is discussed. Our analysis surfaces the need to discuss research about online disinformation and manipulation in a rigorous, unbiased, and responsible way. This article bolsters such effort by identifying and refuting common fallacious arguments used by both proponents and opponents of social bots research, as well as providing directions toward sound methodologies for future research in the field.

الوصول الحر: http://arxiv.org/abs/2303.17251Test

المؤلفون: Aiyappa, Rachith, DeVerna, Matthew R., Pote, Manita, Truong, Bao Tran, Zhao, Wanying, Axelrod, David, Pessianzadeh, Aria, Kachwala, Zoher, Kim, Munjung, Seckin, Ozgur Can, Kim, Minsuk, Gandhi, Sunny, Manikonda, Amrutha, Pierri, Francesco, Menczer, Filippo, Yang, Kai-Cheng

مصطلحات موضوعية: Computer Science - Social and Information Networks

الوصف: 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.
Comment: 8 pages, 3 figures, forthcoming in ICWSM23

الوصول الحر: http://arxiv.org/abs/2301.06287Test