Detection of Negative Campaign in Israeli Municipal Elections (2022)

Abstract: Political competitions are complex settings where candidates use campaigns to promote their chances to be elected. One choice focuses on conducting a positive campaign that highlights the candidate’s achievements, leadership skills, and future programs. The alternative is to focus on a negative campaign that emphasizes the negative aspects of the competing person and is aimed at offending opponents or the opponent’s supporters. In this proposal, we concentrate on negative campaigns in Israeli elections. This work introduces an empirical case study on automatic detection of negative campaigns, using machine learning and natural language processing approaches, applied to the Hebrew-language data from Israeli municipal elections. Our contribution is multi-fold: (1) We provide TONIC—daTaset fOr Negative polItical Campaign in Hebrew—which consists of annotated posts from Facebook related to Israeli municipal elections; (2) We introduce results of a case study, that explored several research questions. RQ1 : Which classifier and representation perform best for this task? We employed several traditional classifiers which are known for their good performance in IR tasks and two pre-trained models based on BERT architecture; several standard representations were employed with traditional ML models. RQ2 : Does a negative campaign always contain offensive language? Can a model, trained to detect offensive language, also detect negative campaigns? We are trying to answer this question by reporting results for the transfer learning from a dataset annotated with offensive language to our dataset.

## introduction political competitions aim at promoting the candidates' chances to be elected. the main decision in such competitions regards the nature of the campaign -that is, whether a candidate should apply a positive campaign that highlights the candidate's achievements, leadership skills, and future programs, or focus on a negative campaign that emphasizes the negative sides of the competitors @xcite @xcite . our work introduces a new dataset of facebook posts, published by candidates during municipal elections in israel. we annotated this dataset with binary labels, where we distinguish between negative campaigns and other campaign-related content. in addition to the dataset, we report the results of extensive experiments, aimed at answering multiple research questions: which supervised model and representation are more effective at automatically detecting negative campaigns? can we effectively detect negative campaigns with a model trained to identify offensive language? can sentiment analysis boost negative campaign detection? our contribution is multi-fold: (1) we introduce a new annotated dataset in hebrew for negative campaign detection; (2) we report results of multiple classifiers and their combination with various representations on our dataset; (3) we explore possible relations between sentiment analysis and negative campaign and (4) between offensive language and negative campaign. ## related work the scholarly literature has investigated various aspects related to negative campaigns @xcite @xcite . it points out that this phenomenon exists in many areas such as competition over jobs in the workplace, yet in the politi-cal arena there are several special characteristics. a major characteristic is a fact that participants in electoral competition often hold positions of power as well as public and private resources to finance their efforts @xcite . in many cases, they also set the rules of the competition as opposed to other areas where the contest organizer sets the rules of the game. indeed, in recent years, we witness more and more political candidates that do not play by the rules, both formally and informally. a specific feature of this trend is the intensive use of negative campaigns which target the weaknesses and failures of the opponents promising to do the opposite @xcite @xcite . the implementation of language technologies in the political sciences is recently in high demand. while computational political scientists are looking for nlp tools to assist automatic analysis of campaign-related content and predict outcomes, computational linguistics explores real-world use cases in political domains. the recent workshop on natural language processing for political sciences (politicalnlp) @xcite is an example of the rising popularity of this interdisciplinary research. however, despite some works dedicated to the analysis of elections-related materials @xcite @xcite , in this workshop or anywhere else, we were unable to find any work on automated negative campaign analysis and detection. as a majority of text classification tasks last years are efficiently performed by pre-trained language models and transformers, we follow this approach in our study. we apply bert, its multilingual (mbert) and hebrew (alephbert) versions. mbert serves us both as an encoder (feature extractor) and end-to-end classifier. 1 in addition to the introduction of a new dataset, we explore possible relations between sentiment analysis and negative campaigns and between offensive language and negative campaigns. ## future work and conclusions in this paper, we introduce a new dataset that can help researchers to study negative campaigns. the dataset contains only hebrew-written content posted by israeli politicians on facebook. we report the results of extensive experiments which include multiple classifiers and representations and answer two research questions: whether transfer learning from offensive language to negative campaign can be efficiently applied and whether sentiment analysis can boost negative campaign detection. we can conclude that traditional models with unigrams and sentiment labels as text representations performed best in both scenarios. this is probably due to a small training set which is not sufficient for efficient fine-tuning of pre-trained transformers with a large number of parameters, but big enough to train a relatively simple classification function with fewer parameters. also, unigrams seem to be most efficient in representing hebrew texts -due to the rich morphology of hebrew and ambiguous tokenization, simple bow (and tf-idf) cannot provide enough semantic information. it also might be the case of political rhetoric which is similar across candidates and campaigns of different political parties. based on our results, we can conclude that sentiment analysis obviously boosts negative campaign detection. however, there is no strong relation between offensive language and negative campaigns. therefore, transfer learning with models trained to detect offensive content is inefficient for the detection of a negative campaign. in the future, we plan to apply our analysis to elections to the israeli government. we also would like to see whether cross-lingual and cross-country learning is efficient for negative campaign detection. we'd like to explore the common characteristics and differences between political campaigns in different countries. we hypothesize that an engagement of a candidate in a negative campaign can be dependent on the candidate's gender, perceived strength, initial support, etc. we intend to study these possible relations in the future.