Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science Conference (WebSci 2020). Please cite the WebSci version; Second version includes corrected typo

Measuring Voter's Candidate Preference Based on Affective Responses to Election Debates

In this paper we present the first analysis of facial responses to electoral debates measured automatically over the Internet. We show that significantly different responses can be detected from viewers with different political preferences and that similar expressions at significant moments can have very different meanings depending on the actions that appear subsequently. We used an Internet based framework to collect 611 naturalistic and spontaneous facial responses to five video clips from the 3rd presidential debate during the 2012 American presidential election campaign. Using this framework we were able to collect over 60% of these video responses (374 videos) within one day of the live debate and over 80% within three days. No participants were compensated for taking the survey. We present and evaluate a method for predicting independent voter preference based on automatically measured facial responses and self-reported preferences from the viewers. We predict voter preference with an average accuracy of over 73% (AUC 0.779)

Classification Of Ad Tone in Political Video Advertisements Under Class Imbalance and Low Data Samples

Ad tone defines the aim of a political video advertisement, which can be either to promote a specific candidate, to attack the candidates or to contrast the candidates. Depending upon the aim, a political video advertisement can be classified into either promote, attack or contrast class. Analysis of ad tone in political video advertisements can provide more insights about the political campaign to political science researchers. Political campaigns are investing more and more on online platforms, which creates a large amount of political video advertisements. Manual classification of ad tones in political video advertisement is time-consuming, labor intensive and not scalable. Hence, there is a need for an efficient and effective classification model for automatic classification of the ad tones in political video advertisements. The available labeled dataset is very small in size and suffers from class imbalance. Due to this reason, the performance of the minority class is poor compared to the majority class. Moreover, due to the way the different classes are defined, all three classes decompose into sub-parts and suffer from class overlapping problem. There has been an attempt in automatic classification of political ad tones, but it does not take class imbalance into account. We investigate a couple of data augmentation techniques to overcome the class imbalance problem and the effectiveness of deep learning models on ad tone classification using text-based features. In our experiments, the best deep learning model offers a better F1 score of 0.570 on the minority class compared to the F1 score of previous work, which is 0.527. However, the performance is still unsatisfactory. We design hand-crafted features specific for ad tone classification using Support Vector Machine as the classifier. Our proposed approach gives the best weighted average F1 score of 0.860 on the entire test set and F1 score of 0.657 on the minority contrast class

When users control the algorithms: Values expressed in practices on the twitter platform

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices

Facing Forward: Policy for Automated Facial Expression Analysis

The human face is a powerful tool for nonverbal communication. Technological advances have enabled widespread and low-cost deployment of video capture and facial recognition systems, opening the door for automated facial expression analysis (AFEA). This paper summarizes current challenges to the reliability of AFEA systems and challenges that could arise as a result of reliable AFEA systems. The potential benefits of AFEA are considerable, but developers, prospective users, and policy makers should proceed with caution

On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads in 2022 French Election

Online political advertising has become the cornerstone of political campaigns. The budget spent solely on political advertising in the U.S. has increased by more than 100% from \$700 million during the 2017-2018 U.S. election cycle to \$1.6 billion during the 2020 U.S. presidential elections. Naturally, the capacity offered by online platforms to micro-target ads with political content has been worrying lawmakers, journalists, and online platforms, especially after the 2016 U.S. presidential election, where Cambridge Analytica has targeted voters with political ads congruent with their personality To curb such risks, both online platforms and regulators (through the DSA act proposed by the European Commission) have agreed that researchers, journalists, and civil society need to be able to scrutinize the political ads running on large online platforms. Consequently, online platforms such as Meta and Google have implemented Ad Libraries that contain information about all political ads running on their platforms. This is the first step on a long path. Due to the volume of available data, it is impossible to go through these ads manually, and we now need automated methods and tools to assist in the scrutiny of political ads. In this paper, we focus on political ads that are related to policy. Understanding which policies politicians or organizations promote and to whom is essential in determining dishonest representations. This paper proposes automated methods based on pre-trained models to classify ads in 14 main policy groups identified by the Comparative Agenda Project (CAP). We discuss several inherent challenges that arise. Finally, we analyze policy-related ads featured on Meta platforms during the 2022 French presidential elections period.Comment: Proceedings of the ACM Web Conference 2023 (WWW '23), May 1--5, 2023, Austin, TX, US

Transparency in Political Advertising: Assessing the Utility and Validity of the FCC\u27s Online Public Inspection File System

This research explores the usability of the Federal Communication Commission\u27s (FCC\u27s) online Public Inspection Files to measure the sources and quantities of political advertising on broadcast television. We compared data from FCC files with data purchased from a commercial vendor in a presidential caucus campaign that stretched across nine months, including advertising sponsored by over 40 groups and totaled tens of millions of dollars. The FCC-derived and commercial data were consistent in reporting the quantity of advertising, but sponsor identification was inconsistent between data sources, raising concerns about the FCC\u27s ability to disclose reliable information about political ad spending

Valências na resposta emocional dos eleitores: design experimental com neurociência

This work aims to quantitatively and qualitatively evaluate the valence of voters’ emotional response to changes in the scenarios in videos of political propaganda. The experiment was conducted in a laboratory with a fictitious candidate and content. We used four different scenarios: one with a completely white background, one simulating a library, one with a popular house, and one with luxury houses. We use the Facial Action Coding System (FACS) as an instrument to measure emotions. We found statistical differences between the intensity of the valences throughout the video (n=108). The work empirically demonstrated that the scenarios can enhance the emotional effects of this type of advertising.Este trabalho tem como objetivo avaliar quantitativa e qualitativamente a valência da resposta emocional dos eleitores às alterações dos cenários nos vídeos de propaganda política. O experimento foi conduzido em laboratório com um candidato e conteúdo fictícios. Utilizamos quatro cenários diferentes: um cenário com fundo completamente branco, um simulando uma biblioteca, um com casa popular e outro com casas de luxo. Utilizamos o Sistema de Codificação de Ação Facial (FACS) como um instrumento para medir emoções. Encontramos diferenças estatísticas entre a intensidade das valências ao longo do vídeo (n = 108). O trabalho permitiu a demonstração empírica de que os cenários podem potencializar os efeitos emocionais desse tipo de publicidade

Exploring 'smart citizenship' as a socio-technical ecology: the case of Oxfordshire, UK

Critical social science scholarship on ‘smart citizenship’ has thus far emphasised ‘bottom-up’ participation as a democratising antidote to ‘top-down’ corporate or state-led smart cities. It is implied that contesting these powerful smart actors involves increasing the degree of citizen participation in smart programmes or projects and by enabling greater political agency in grassroots or citizen-centric alternatives. In this thesis, I emphasise the multiple and heterogenous ways ‘smart citizenship’ is enacted through a diverse set of discourses, practices, and materialities. Approaching these collectives as ‘socio-technical ecologies’, I seek to move beyond existing dichotomies that frame smart citizenship as either a condition of technologically-mediated authoritarian control (top-down) or of increased democratic participatory processes (bottom-up). My approach, I argue, helps to account for a wider set of interrelated ways in which citizenship is negotiated in actually-existing contexts of the smart city. The thesis draws on empirical materials generated through a study of how the UK county of Oxfordshire is being made ‘smart’. In doing so, I identify four overlapping, interconnected ways in which smart citizenship is constituted through ecologies of discourses, practices and materialities. The first is a type of ‘informational’ smart citizenship, which is centred on establishing and mobilising a fairly familiar mix of participatory deliberative engagement practices, procedures, and technologies. The second is the primarily discursive framing of citizens as living lab ‘beneficiaries’ who accrue relative advantages from experiments with technological products or services. Beneficiary citizens are enrolled in political-economic discourses of innovation to legitimise imaginaries of anticipated smart futures. The third raises the importance of 'expert' citizenship, which is deployed by partners to constitute local tech workers as experts engaged in making Oxford smart. I finally consider the ‘sim’ citizenships produced from machine learning methods of data analysis generative of road actor behaviour models for digital twin modelling. Sim citizens, calibrated by smart city data, populate the digital twin for iterative validation and verification testing of automated driving systems. The thesis altogether contributes to scholarly understandings of smart city citizenship by identifying emerging sets of relations between humans and technologies in digitally-mediated cities