772 research outputs found
Social Bots for Online Public Health Interventions
According to the Center for Disease Control and Prevention, in the United
States hundreds of thousands initiate smoking each year, and millions live with
smoking-related dis- eases. Many tobacco users discuss their habits and
preferences on social media. This work conceptualizes a framework for targeted
health interventions to inform tobacco users about the consequences of tobacco
use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that
leverages machine learning to identify users posting pro-tobacco tweets and
select individualized interventions to address their interest in tobacco use.
We searched the Twitter feed for tobacco-related keywords and phrases, and
trained a convolutional neural network using over 4,000 tweets dichotomously
manually labeled as either pro- tobacco or not pro-tobacco. This model achieves
a 90% recall rate on the training set and 74% on test data. Users posting pro-
tobacco tweets are matched with former smokers with similar interests who
posted anti-tobacco tweets. Algorithmic matching, based on the power of peer
influence, allows for the systematic delivery of personalized interventions
based on real anti-tobacco tweets from former smokers. Experimental evaluation
suggests that our system would perform well if deployed. This research offers
opportunities for public health researchers to increase health awareness at
scale. Future work entails deploying the fully operational Notobot system in a
controlled experiment within a public health campaign
A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksDuring the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well as sensitive content throughout the network has fostered applied researh to automatically measure the reliability of social networks accounts via Artificial Intelligence (AI). In this paper, we present a multilingual approach for addressing the bot identification task in Twitter via Deep learning (DL) approaches to support end-users when checking the credibility of a certain Twitter account. To do so, several experiments were conducted using state-of-the-art Multilingual Language Models to generate an encoding of the text-based features of the user account that are later on concatenated with the rest of the metadata to build a potential input vector on top of a Dense Network denoted as Bot-DenseNet. Consequently, this paper assesses the language constraint from previous studies where the encoding of the user account only considered either the metadatainformation or the metadata information together with some basic semantic text features. Moreover, the Bot-DenseNet produces a low-dimensional representation of the user account which can be used for any application within the Information Retrieval (IR) framewor
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
Social media bot detection with deep learning methods: a systematic review
Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection
Neural networks are increasingly used for graph classification in a variety
of contexts. Social media is a critical application area in this space, however
the characteristics of social media graphs differ from those seen in most
popular benchmark datasets. Social networks tend to be large and sparse, while
benchmarks are small and dense. Classically, large and sparse networks are
analyzed by studying the distribution of local properties. Inspired by this, we
introduce Graph-Hist: an end-to-end architecture that extracts a graph's latent
local features, bins nodes together along 1-D cross sections of the feature
space, and classifies the graph based on this multi-channel histogram. We show
that Graph-Hist improves state of the art performance on true social media
benchmark datasets, while still performing well on other benchmarks. Finally,
we demonstrate Graph-Hist's performance by conducting bot detection in social
media. While sophisticated bot and cyborg accounts increasingly evade
traditional detection methods, they leave artificial artifacts in their
conversational graph that are detected through graph classification. We apply
Graph-Hist to classify these conversational graphs. In the process, we confirm
that social media graphs are different than most baselines and that Graph-Hist
outperforms existing bot-detection models
STREAM-EVOLVING BOT DETECTION FRAMEWORK USING GRAPH-BASED AND FEATURE-BASED APPROACHES FOR IDENTIFYING SOCIAL BOTS ON TWITTER
This dissertation focuses on the problem of evolving social bots in online social networks, particularly Twitter. Such accounts spread misinformation and inflate social network content to mislead the masses. The main objective of this dissertation is to propose a stream-based evolving bot detection framework (SEBD), which was constructed using both graph- and feature-based models. It was built using Python, a real-time streaming engine (Apache Kafka version 3.2), and our pretrained model (bot multi-view graph attention network (Bot-MGAT)). The feature-based model was used to identify predictive features for bot detection and evaluate the SEBD predictions. The graph-based model was used to facilitate multiview graph attention networks (GATs) with fellowship links to build our framework for predicting account labels from streams. A probably approximately correct learning framework was applied to confirm the accuracy and confidence levels of SEBD.The results showed that the SEBD can effectively identify bots from streams and profile features are sufficient for detecting social bots. The pretrained Bot-MGAT model uses fellowship links to reveal hidden information that can aid in identifying bot accounts. The significant contributions of this study are the development of a stream based bot detection framework for detecting social bots based on a given hashtag and the proposal of a hybrid approach for feature selection to identify predictive features for identifying bot accounts. Our findings indicate that Twitter has a higher percentage of active bots than humans in hashtags. The results indicated that stream-based detection is more effective than offline detection by achieving accuracy score 96.9%. Finally, semi supervised learning (SSL) can solve the issue of labeled data in bot detection tasks
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