Related papers: Prediction of Facebook Post Metrics using Machine …

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on…

Engaged costumers are a very import part of current social media marketing. Public figures and brands have to be very careful about what to post online. That is why the need for accurate strategies for anticipating the impact of a post…

Since the introduction of social media, user participation or engagement has received little research attention. In this survey article, we establish the notion of participation in social media and main challenges that researchers may face…

In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain…

Basic human values represent a set of values such as security, independence, success, kindness, and pleasure, which we deem important to our lives. Each of us holds different values with different degrees of significance. Existing studies…

Classification of social media posts in emergency response is an important practical problem: accurate classification can help automate processing of such messages and help other responders and the public react to emergencies in a timely…

The widespread use of social media highlights the need to understand its impact, particularly the role of online social support. This study uses a dataset focused on online social support, which includes binary and multiclass…

There is a large amount of interest in understanding users of social media in order to predict their behavior in this space. Despite this interest, user predictability in social media is not well-understood. To examine this question, we…

Social media creates crucial mass changes, as popular posts and opinions cast a significant influence on users' decisions and thought processes. For example, the recent Reddit uprising inspired by r/wallstreetbets which had remarkable…

Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the perfor- mance of a wide variety of social network based measurements…

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider…

News items have a significant impact on stock markets but the ways are obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online…

This paper surveys mathematical models, structural results and algorithms in controlled sensing with social learning in social networks. Part 1, namely Bayesian Social Learning with Controlled Sensing addresses the following questions: How…

In this paper, we investigate the effectiveness of unsupervised feature learning techniques in predicting user engagement on social media. Specifically, we compare two methods to predict the number of feedbacks (i.e., comments) that a blog…

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data contributed to significant evolution of social network research and link prediction techniques, which are…

This article improves the Transformer model based on swarm intelligence optimization algorithm, aiming to predict the emotions of employment related text content on American social media. Through text preprocessing, feature extraction, and…

This study examines how fake news affects social media users across a range of age groups and how machine learning (ML) and artificial intelligence (AI) can help reduce the spread of false information. The paper evaluates various machine…

We present a machine learning approach for predicting social media engagement (comments and likes) from emotional and temporal features. The dataset contains 600 songs with annotations for valence, arousal, and related sentiment metrics. A…

The problem of predicting people's participation in real-world events has received considerable attention as it offers valuable insights for human behavior analysis and event-related advertisement. Today social networks (e.g. Twitter)…

Social Media Popularity Prediction has drawn a lot of attention because of its profound impact on many different applications, such as recommendation systems and multimedia advertising. Despite recent efforts to leverage the content of…