Analyzing Political Polarization on Social Media by Deleting Bot Spamming

Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users.

The Consequences of Digitally Driven Changes in Political Campaigning for Democratic Societies: A Case Study of the 2020 US Presidential Election

The effect from COVID-19 pandemic has changed how presidential candidates do their political campaigns. The restriction to do social distancing makes the usual campaign not doable. That’s why presidential candidates need to find another way for their political campaign, which is by doing things digitally. This digitally driven changes can have its advantages and disadvantages. In this paper we discuss about the consequences of the changes in political campaigns in digital form or through social media for democratic societies in US presidential election. We use qualitative descriptive with case study method. In this paper we use secondary data such as research journals that’s related to this topic, documentation and articles. We find that the changes to digital campaigning have its own pros and cons that can affect how politicians do their campaigns on their social media platforms.

Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject’s historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day’s 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016–2021. We measure Trump’s narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy—the rate at which a population’s stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd’s murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography.

Studying the Community of Trump Supporters on Twitter during the 2020 US Presidential Election via Hashtags #maga and #trump2020

(1) The study investigated the social network surrounding the hashtags #maga (Make America Great Again, the campaign slogan popularized by Donald Trump during his 2016 and 2020 presidential campaigns) and #trump2020 on Twitter to better understand Donald Trump, his community of supporters, and their political discourse and activities in the political context of the 2020 US presidential election. (2) Social network analysis of a sample of 220,336 tweets from 96,820 unique users, posted between 27 October and 2 November 2020 (i.e., one week before the general election day) was conducted. (3) The most active and influential users within the #maga and #trump2020 network, the likelihood of those users being spamming bots, and their tweets’ content were revealed. (4) The study then discussed the hierarchy of Donald Trump and the problematic nature of spamming bot detection, while also providing suggestions for future research.

Trolls and Hacktivists

Online communities have long been the sites of political mobilization. Work on these communities in relation to politics sits at the intersection of the study of social movements broadly as well as hacktivism specifically; anthropological and cultural studies of online culture, including trolling; and work focused on the affordances of social platforms. Drawing on four linked cases of online community mobilization—4chan and trolling culture, Anonymous, Gamergate, and the 2016 US presidential election—the author discusses this varied theory and its ability to contribute to the understanding of online communities as a political and social phenomenon. The author illustrates that there are distinct repertoires of contention that emerged from 4chan prior to 2008 that subsequent movements refined and adapted. The author argues that 4chan’s affordances created a cultural identity that was durable, with shared discourse, affirmation of group values, and a history of collective action that served as a base for mobilization. These modes of collective action, including organized harassment, have since been adopted by a range of political actors. Future research should address questions of movement durability, emergence, and the interplay between internet affordances and offline contexts.

Proposing a model of social media user interaction with fake news

Purpose This study aims to understand the allure and danger of fake news in social media environments and propose a theoretical model of the phenomenon. Design/methodology/approach This qualitative research study used the uses and gratifications theory (UGT) approach to analyze how and why people used social media during the 2016 US presidential election. Findings The thematic analysis revealed people were gratified after using social media to connect with friends and family and to gather and share information and after using it as a vehicle of expression. Participants found a significant number of fake news stories on social media during the 2016 US presidential election. Participants tried to differentiate between fake news and real news using fact-checking websites and news sources and interacted with the social media users who posted fake news and became part of the echo chamber. Behaviors like these emerged in the analysis that could not be completely explained by UGT and required further exploration which resulted in a model that became the core of this study. Research limitations/implications This is a small-scale exploratory study with eight diverse participants, findings should not be generalized to larger populations. Time-specific self-reporting of information from social media and fake news during the 2016 US presidential election. Upgrading public policies related to social media is recommended in the study, contributing to burgeoning policy discussions and provides recommendations for both purveyors of social media and public policymakers. Practical implications Upgrade in public policies related to social media is recommended in the study and contributes to burgeoning policy discussions and provides recommendations for both purveyors of social media and public policymakers. Social implications Social media users are spending increased time on their preferred platforms. This study increases the understanding of the nature, function and transformation of virtual social media environments and their effects on real individuals, cultures and societies.What is original/of value about the paper?This exploratory study establishes the foundation on which to expand research in the area of social media use and fake news. Originality/value This exploratory study establishes the foundation to expand research in the area of social media use and fake news.

No evidence for systematic voter fraud: A guide to statistical claims about the 2020 election

After the 2020 US presidential election Donald Trump refused to concede, alleging widespread and unparalleled voter fraud. Trump’s supporters deployed several statistical arguments in an attempt to cast doubt on the result. Reviewing the most prominent of these statistical claims, we conclude that none of them is even remotely convincing. The common logic behind these claims is that, if the election were fairly conducted, some feature of the observed 2020 election result would be unlikely or impossible. In each case, we find that the purportedly anomalous fact is either not a fact or not anomalous.