TIES: Temporal Interaction Embeddings For Enhancing Social Media
Integrity At Facebook
- URL: http://arxiv.org/abs/2002.07917v1
- Date: Tue, 18 Feb 2020 22:56:40 GMT
- Title: TIES: Temporal Interaction Embeddings For Enhancing Social Media
Integrity At Facebook
- Authors: Nima Noorshams, Saurabh Verma, Aude Hofleitner
- Abstract summary: We present a novel Temporal Interaction EmbeddingS model that is designed to capture rogue social interactions and flag them for further suitable actions.
TIES is a supervised, deep learning, production ready model at Facebook-scale networks.
To show the real-world impact of TIES, we present a few applications especially for preventing spread of misinformation, fake account detection, and reducing ads payment risks.
- Score: 9.023847175654602
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Since its inception, Facebook has become an integral part of the online
social community. People rely on Facebook to make connections with others and
build communities. As a result, it is paramount to protect the integrity of
such a rapidly growing network in a fast and scalable manner. In this paper, we
present our efforts to protect various social media entities at Facebook from
people who try to abuse our platform. We present a novel Temporal Interaction
EmbeddingS (TIES) model that is designed to capture rogue social interactions
and flag them for further suitable actions. TIES is a supervised, deep
learning, production ready model at Facebook-scale networks. Prior works on
integrity problems are mostly focused on capturing either only static or
certain dynamic features of social entities. In contrast, TIES can capture both
these variant behaviors in a unified model owing to the recent strides made in
the domains of graph embedding and deep sequential pattern learning. To show
the real-world impact of TIES, we present a few applications especially for
preventing spread of misinformation, fake account detection, and reducing ads
payment risks in order to enhance the platform's integrity.
Related papers
- Decoding the Silent Majority: Inducing Belief Augmented Social Graph
with Large Language Model for Response Forecasting [74.68371461260946]
SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics.
Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
arXiv Detail & Related papers (2023-10-20T06:17:02Z) - Trust and Believe -- Should We? Evaluating the Trustworthiness of
Twitter Users [5.695742189917657]
Fake news on social media is a major problem with far-reaching negative repercussions on both individuals and society.
In this work, we create a model through which we hope to offer a solution that will instill trust in social network communities.
Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user.
arXiv Detail & Related papers (2022-10-27T06:57:19Z) - Cross-Network Social User Embedding with Hybrid Differential Privacy
Guarantees [81.6471440778355]
We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way.
In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types.
To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
arXiv Detail & Related papers (2022-09-04T06:22:37Z) - SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian
Trajectory Prediction [59.064925464991056]
We propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN)
SSAGCN aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments.
Experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
arXiv Detail & Related papers (2021-12-05T01:49:18Z) - Hater-O-Genius Aggression Classification using Capsule Networks [6.318682674371969]
We propose an end-to-end ensemble-based architecture to automatically identify and classify aggressive tweets.
Tweets are classified into three categories - Covertly Aggressive, Overtly Aggressive, and Non-Aggressive.
Our best model is an ensemble of Capsule Networks and results in a 65.2% F1 score on the Facebook test set, which results in a performance gain of 0.95% over the TRAC-2018 winners.
arXiv Detail & Related papers (2021-05-24T11:53:58Z) - How Social Are Social Media The Dark Patterns In Facebook's Interface [9.824986063639155]
We investigate Facebook using the tools of HCI to find connections between interface features and the concerns raised by these domains.
With a nod towards Dark Patterns, we use an empirical design analysis to identify interface interferences that impact users' online privacy.
arXiv Detail & Related papers (2021-03-19T10:40:29Z) - PHASE: PHysically-grounded Abstract Social Events for Machine Social
Perception [50.551003004553806]
We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions.
Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events.
As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
arXiv Detail & Related papers (2021-03-02T18:44:57Z) - Preserving Integrity in Online Social Networks [13.347579281117628]
This paper surveys the state of the art in keeping online platforms and their users safe from such harm.
We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community.
arXiv Detail & Related papers (2020-09-22T04:32:24Z) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z) - I Know Where You Are Coming From: On the Impact of Social Media Sources
on AI Model Performance [79.05613148641018]
We will study the performance of different machine learning models when being learned on multi-modal data from different social networks.
Our initial experimental results reveal that social network choice impacts the performance.
arXiv Detail & Related papers (2020-02-05T11:10:44Z) - Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics [43.98568073610101]
We use a social media model to quantify the impacts of several adversarial manipulation tactics on the quality of content.
We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation.
These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
arXiv Detail & Related papers (2019-07-13T21:12:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.