604 research outputs found
Tensor Factorization with Label Information for Fake News Detection
The buzz over the so-called "fake news" has created concerns about a
degenerated media environment and led to the need for technological solutions.
As the detection of fake news is increasingly considered a technological
problem, it has attracted considerable research. Most of these studies
primarily focus on utilizing information extracted from textual news content.
In contrast, we focus on detecting fake news solely based on structural
information of social networks. We suggest that the underlying network
connections of users that share fake news are discriminative enough to support
the detection of fake news. Thereupon, we model each post as a network of
friendship interactions and represent a collection of posts as a
multidimensional tensor. Taking into account the available labeled data, we
propose a tensor factorization method which associates the class labels of data
samples with their latent representations. Specifically, we combine a
classification error term with the standard factorization in a unified
optimization process. Results on real-world datasets demonstrate that our
proposed method is competitive against state-of-the-art methods by implementing
an arguably simpler approach.Comment: Presented at the Workshop on Reducing Online Misinformation Exposure
ROME 201
Social Media as a Medium to Promote Local Perception Expression in China’s World Heritage Sites
The assessment of public participation is one of the most fundamental components of holistic and sustainable cultural heritage management. Since the beginning of 2020, the COVID-19 pandemic became a catalyst for the transformation of participatory tools. Collaboration with stakeholders moved online due to the strict restrictions preventing on-site activities. This phenomenon provided an opportunity to formulate more comprehensive and reasonable urban heritage protection strategies. However, very few publications mentioned how social networking sites’ data could support humanity-centred heritage management and participatory evaluation. Taking five World Cultural Heritage Sites as research samples, the study provides a methodology to evaluate online participatory practices in China through Weibo, a Chinese-originated social media platform. The data obtained were analysed from three perspectives: the users’ information, the content of texts, and the attached images. As shown in the results section, individuals’ information is described by gender, geo-location, celebrities, and Key Opinion Leaders. To a greater extent, participatory behaviour emerges at the relatively primary levels, that being “informing and consulting”. According to the label detection of Google Vision, residents paid more attention to buildings, facades, and temples in the cultural heritage sites. The research concludes that using social media platforms to unveil interplays between digital and physical heritage conservation is feasible and should be widely encouraged
Online Crowds Opinion-Mining it to Analyze Current Trend: A Review
Online presence of the user has increased, there is a huge growth in the number of active users and thus the volume of data created on the online social networks is massive. Much are concentrating on the Internet Lingo. Notably most of the data on the social networking sites is made public which opens doors for companies, researchers and analyst to collect and analyze the data. We have huge volume of opinioned data available on the web we have to mine it so that we could get some interesting results out of it with could enhance the decision making process. In order to analyze the current scenario of what people are thinking focus is shifted towards opinion mining. This study presents a systematic literature review that contains a comprehensive overview of components of opinion mining, subjectivity of data, sources of opinion, the process and how does it let one analyze the current tendency of the online crowd in a particular context. Different perspectives from different authors regarding the above scenario have been presented. Research challenges and different applications that were developed with the motive opinion mining are also discussed
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning
The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with
state-of-the-art methods.Comment: Accepted by SIGIR 202
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