Adapting Emotion Detection to Analyze Influence Campaigns on Social Media (2023)

Abstract: Social media is an extremely potent tool for influencing public opinion, particularly during important events such as elections, pandemics, and national conflicts. Emotions are a crucial aspect of this influence, but detecting them accurately in the political domain is a significant challenge due to the lack of suitable emotion labels and training datasets. In this paper, we present a generalized approach to emotion detection that can be adapted to the political domain with minimal performance sacrifice. Our approach is designed to be easily integrated into existing models without the need for additional training or fine-tuning. We demonstrate the zero-shot and few-shot performance of our model on the 2017 French presidential elections and propose efficient emotion groupings that would aid in effectively analyzing influence campaigns and agendas on social media.

## introduction digital environments, such as social media, are powerful launching platforms for wide-reaching influence campaigns surrounding important events such as elections, pandemics, and armed conflicts, as well as commercial interests @xcite @xcite . these campaigns aim to manipulate public opinion in a particular way: to favor or oppose a political candidate, to accept or resist vaccination, to justify an aggression, etc. this is achieved by disseminating messages that advance a specific agenda, using language, imagery, and topics that are likely to resonate with the target audience. presidential elections offer a substantial context for examining influence campaigns on social media platforms and is the focus of this study. various indicators, such as agenda, stance, concern, belief, emotion, and imageability, have been identified for measuring the influence of social media messages within this context @xcite . emotion is deeply integrated in political discourse and is used as a rhetorical tool in persuading the audience @xcite . emotionally charged messages can significantly sway public opinion regarding specific agendas or candidates @xcite and several studies have documented the effect of emotional language in disseminating polarizing content via social media platforms @xcite . existing social media datasets, especially those focused on election-related messages posted on twitter, are labeled using traditional emotion categories derived from @xcite . these datasets facilitate the development of emotion analysis tools and apply them on diverse applications ranging from healthcare @xcite and education @xcite to stock market @xcite and political opinion mining @xcite . however, each new application domain presents its own set of challenges that existing systems are unable to handle. therefore, when a new emotion detection problem emerges in a specialized domain, researchers engage in an exhaustive annotation process to build relevant datasets. this highlights the necessity for enhancing the flexibility and robustness of existing models in order to accommodate new scenarios. potential solutions involve using semisupervised, unsupervised, zero-shot, or few-shot techniques @xcite @xcite . nevertheless, solely relying on emotion labels and their definitions from external resources, such as wordnet @xcite , are insufficient to capture the intricate concepts and subtleties associated with each emotion label when viewed through the lens of the application domain. psychological theories suggest that emotion definitions are not universally applicable across domains or individuals, rather, they are profoundly shaped by the socio-cultural context and specific events @xcite , emphasizing the need to incorporate domain-specific knowledge and emotion inter dependencies for effective zero-shot systems. but which emotions matter in an influence campaign? do the same emotions arise when discussing a new electronic gadget on the market as when comparing political candidates ahead of an election? in this paper, we present a novel zeroshot approach to detect emotions in text, adaptable to unexplored domains or target label sets. our method incorporates interpretations of emotion labels and their inter dependencies for improved results in the target domain. we investigate tweets around the 2017 french presidential elections part of which is publicly available on kaggle (daignan, 2017) and thoroughly evaluate our method to demonstrate that it addresses the shortfalls of existing zero-shot approaches. this is an important step towards providing valuable insights on the emotions of the audience towards political campaigns and agendas. ## methodology upon completing a preliminary investigation of the 2017 french election dataset, our annotation team observed that assigning a distinct emotion label to each tweet is a challenging and a complex task. following a more practical approach, we label tweets using groups of emotions that frequently co-occur or overlap @xcite . these groups of emotions are combinations of the traditional emotion labels and are difficult to isolate from short informal tweets. in instances where a message cannot be classified into any of the emotion groups but still conveys a strong positive or negative sentiment, it is assigned a "positive-other" or "negative-other" label. the following is the final set e of grouped emotion labels: ## experiments in this section, we compare our approach against popular semi-supervised and zero-shot techniques. all experiments have been carried out on the french election dataset in the below configurations: ## emotions as an influence indicator we use our emotion detection approach to understand how emotions correlate with other influence indicators during political campaigns. we select a subset of the tweets that are associated with specific agendas in the election. figure 5a shows the emotion distribution across tweets mentioning popular candidates. as expected, the predominant emotions are anger, contempt, disgust and optimism, hope signifying that political campaigns either influence the audience by expressing hope/optimism for a brighter future or by expressing hatred towards the opposing candidate or political party. interestingly, although a large portion of the tweets express some strong positive/negative emotion, they cannot be accurately tagged with a specific emotion label (fig 3 ). this leads us to hypothesize that a large number of emotion labels may not be required to effectively analyze the emotional influence of political campaigns. figures 4 and 5b illustrates the results of emotion detection on the agenda annotated tweets. in this paper, we focus on the following agendas: 1) believe that an entity (e) or group (g) is immoral or harmful; 2) believe that e/g is moral or beneficial; 3) believe that your group are at risk; 4) believe that your actions can lead to a good outcome or hope; 5) call to share information; 6) call to vote for a e/g; 7) call to vote against a e/g; and 8) call to participate in demonstration/protest or attend a rally/campaign. in figure 4 , higher anger and negative-other scores are observed in the agenda of 'entity is immoral'. in contrast, the 'entity is moral' agenda shows higher admiration and positive-other scores. by comparing the emotions of 'group at risk' and 'belief for good outcome', we find higher anger, fear, and negative-other scores in the former, and higher optimism and positive-other scores in the latter. similarly, the agendas 'vote for entity' and 'vote against entity' have higher positive and negative emotions, respectively. these differences can be clearly seen in fig. 5b that shows the proportion of each emotion in an agenda. we can conclude that emotions play an important role in understanding patterns in a campaign and the impact of political agendas on the audience. ## conclusion our paper presents a generalized approach to emotion detection wherein existing emotion detection datasets and models can be quickly adapted to specialized emotion labels to effectively analyze influence campaigns in the political domain. our experiments demonstrate the efficacy of this zero-shot approach on tweets from the 2017 french presidential election.