A Recipe for Social Media Analysis
- URL: http://arxiv.org/abs/2106.07307v1
- Date: Mon, 14 Jun 2021 11:27:33 GMT
- Title: A Recipe for Social Media Analysis
- Authors: Shahid Alam, Juvariya Khan
- Abstract summary: We present and discuss a high-level functional intelligence model (recipe) of Social Media Analysis (SMA)
This model synthesizes the input data and uses operational intelligence to provide actionable recommendations.
It can be applied to different domains, such as Education, Healthcare and Government, etc.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Ubiquitous nature of smartphones has significantly increased the use of
social media platforms, such as Facebook, Twitter, TikTok, and LinkedIn, etc.,
among the public, government, and businesses. Facebook generated ~70 billion
USD in 2019 in advertisement revenues alone, a ~27% increase from the previous
year. Social media has also played a strong role in outbreaks of social
protests responsible for political changes in different countries. As we can
see from the above examples, social media plays a big role in business
intelligence and international politics. In this paper, we present and discuss
a high-level functional intelligence model (recipe) of Social Media Analysis
(SMA). This model synthesizes the input data and uses operational intelligence
to provide actionable recommendations. In addition, it also matches the
synthesized function of the experiences and learning gained from the
environment. The SMA model presented is independent of the application domain,
and can be applied to different domains, such as Education, Healthcare and
Government, etc. Finally, we also present some of the challenges faced by SMA
and how the SMA model presented in this paper solves them.
Related papers
- OASIS: Open Agent Social Interaction Simulations with One Million Agents [147.2538500202457]
We propose a scalable social media simulator based on real-world social media platforms.
OASIS supports large-scale user simulations capable of modeling up to one million users.
We replicate various social phenomena, including information spreading, group polarization, and herd effects across X and Reddit platforms.
arXiv Detail & Related papers (2024-11-18T13:57:35Z) - SoMeLVLM: A Large Vision Language Model for Social Media Processing [78.47310657638567]
We introduce a Large Vision Language Model for Social Media Processing (SoMeLVLM)
SoMeLVLM is a cognitive framework equipped with five key capabilities including knowledge & comprehension, application, analysis, evaluation, and creation.
Our experiments demonstrate that SoMeLVLM achieves state-of-the-art performance in multiple social media tasks.
arXiv Detail & Related papers (2024-02-20T14:02:45Z) - DeSIQ: Towards an Unbiased, Challenging Benchmark for Social
Intelligence Understanding [60.84356161106069]
We study the soundness of Social-IQ, a dataset of multiple-choice questions on videos of complex social interactions.
Our analysis reveals that Social-IQ contains substantial biases, which can be exploited by a moderately strong language model.
We introduce DeSIQ, a new challenging dataset, constructed by applying simple perturbations to Social-IQ.
arXiv Detail & Related papers (2023-10-24T06:21:34Z) - SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans.
In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals.
We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
arXiv Detail & Related papers (2023-10-18T02:27:01Z) - 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) - SocialVec: Social Entity Embeddings [1.4010916616909745]
This paper introduces SocialVec, a framework for eliciting social world knowledge from social networks.
We learn social embeddings for roughly 200,000 popular accounts from a sample of the Twitter network.
We exploit SocialVec embeddings for gauging the political bias of news sources in Twitter.
arXiv Detail & Related papers (2021-11-05T14:13:01Z) - Detecting Ideal Instagram Influencer Using Social Network Analysis [0.0]
The paper focuses on social network analysis (SNA) for a real-world online marketing strategy.
The study contributes by comparing various centrality measures to identify the most central nodes in the network and uses a linear threshold model to understand the spreading behaviour of individual users.
arXiv Detail & Related papers (2021-07-12T20:53:58Z) - Over a Decade of Social Opinion Mining [1.0152838128195467]
This systematic review focuses on the evolving research area of Social Opinion Mining.
Natural language can be understood in terms of the different opinion dimensions, as expressed by humans.
Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.
arXiv Detail & Related papers (2020-12-05T17:59:59Z) - 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) - Mobile social media usage and academic performance [3.893605812705635]
Students are especially sensitive to social media and smartphones because of their pervasiveness.
Several studies have shown that there is a negative correlation between social media and academic performance.
We propose to bridge this gap by parametrizing social media usage and academic performance.
arXiv Detail & Related papers (2020-04-03T06:14:36Z) - 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)
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.