arXiv subjects

Maria Glenski

Publications and source records attributed to Maria Glenski.

24 records · Page 2Linked to original sources

arXiv2018

Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources

In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to better understand how users react to trusted and deceptive news sources across two popular, and very different, social media platforms. To that end, (1) we develop a model to classify user reactions into one of nine types, such as answer, elaboration, and question, etc, and (2) we measure the speed and the type of reaction for trusted and deceptive news sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there are significant differences in the speed and the type of reactions between trusted and deceptive news sources on Twitter, but far smaller differences on Reddit.

cs.CL↗

arXiv2017

Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present in Twitter posts. Our models allow to answer the question not only whether a post is a clickbait or not, but to what extent it is a clickbait post e.g., not at all, slightly, considerably, or heavily clickbaity using a score ranging from 0 to 1. We evaluate the predictive power of models trained on varied text and image representations extracted from tweets. Our best performing model that relies on the tweet text and linguistic markers of biased language extracted from the tweet and the corresponding page yields mean squared error (MSE) of 0.04, mean absolute error (MAE) of 0.16 and R2 of 0.43 on the held-out test data. For the binary classification setup (clickbait vs. non-clickbait), our model achieved F1 score of 0.69. We have not found that image representations combined with text yield significant performance improvement yet. Nevertheless, this work is the first to present preliminary analysis of objects extracted using Google Tensorflow object detection API from images in clickbait vs. non-clickbait Twitter posts. Finally, we outline several steps to improve model performance as a part of the future work.

cs.LG↗

arXiv2017

Predicting User-Interactions on Reddit

In order to keep up with the demand of curating the deluge of crowd-sourced content, social media platforms leverage user interaction feedback to make decisions about which content to display, highlight, and hide. User interactions such as likes, votes, clicks, and views are assumed to be a proxy of a content's quality, popularity, or news-worthiness. In this paper we ask: how predictable are the interactions of a user on social media? To answer this question we recorded the clicking, browsing, and voting behavior of 186 Reddit users over a year. We present interesting descriptive statistics about their combined 339,270 interactions, and we find that relatively simple models are able to predict users' individual browse- or vote-interactions with reasonable accuracy.

cs.SI↗

arXiv2017

Consumers and Curators: Browsing and Voting Patterns on Reddit

As crowd-sourced curation of news and information become the norm, it is important to understand not only how individuals consume information through social news Web sites, but also how they contribute to their ranking systems. In the present work, we introduce and make available a new dataset containing the activity logs that recorded all activity for 309 Reddit users for one year. Using this newly collected data, we present findings that highlight the browsing and voting behavior of the study's participants. We find that most users do not read the article that they vote on, and that, in total, 73% of posts were rated (ie, upvoted or downvoted) without first viewing the content. We also show evidence of cognitive fatigue in the browsing sessions of users that are most likely to vote.

cs.SI↗

arXiv2016

Rating Effects on Social News Posts and Comments

At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in-vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02% on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings on average, of posts by 5.15% and of comments by 37.4%. Compared to the control group, the probability of reaching a high rating (>=2000) for posts is increased by 24.6% when posts receive the positive treatment and for comments is decreased by 46.6% when comments receive the negative treatment.

cs.SI↗

arXiv2015

Random Voting Effects in Social-Digital Spaces: A case study of Reddit Post Submissions

At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors by their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a large-scale in-vivo experiment in social media. We find that small, random rating manipulations on social media submissions created significant changes in downstream ratings resulting in significantly different final outcomes. Positive treatment resulted in a positive effect that increased the final rating by 11.02% on average. Compared to the control group, positive treatment also increased the probability of reaching a high rating (>=2000) by 24.6%. Contrary to the results of related work we also find that negative treatment resulted in a negative effect that decreased the final rating by 5.15% on average.

cs.SI↗