## 1 Introduction  
Social media is changing the face of communication and culture of societies around the world [1]. Numbers of social media users in India have grown substantially in recent years, despite the low quality of internet services and the occasional interruptions or blocking of social media sites in the country. Multifarious populations in the country have been using online social media to communicate, express opinions, engage with friends, and share information [2, 3, 4]. However, the anonymity and mobility of online social media enable the neteziens behind the screen to easily spread hateful content [5, 6]. Social media platforms, like Facebook and Twitter, are criticized for not doing enough to prevent hate speech (HS) on their platform and have come under pressure to take action against hate speech [7, 8]. In order to control and prohibit hate speech, governments worldwide are framing stringent regulations and keeping the implementation of such policies under surveillance in their ambit [9]. The Indian government further monitors social media content to prevent the spread of harmful information, and restricts online hate speech by interrupting the internet service from time to time and blocking access to those sites [10; 11]. Furthermore, the government has already introduced a law that expands the anti-terrorism law to encompass cyberspace in order to prohibit the dissemination of any terrorizing or obscene information. Even for humans, distinguishing whether a portion of text contains HS is not a simple task [12]. Manual judgement of HS is not only time-consuming but may also introduce subjective perceptions of HS composition [13]. Therefore, the definition of HS is crucial in clearly outlining a rule for the annotation process of the dataset, for the annotator, and in order to make the automatic model evaluation easier [14]. Most of the research on social media defines HS as a language that attacks or diminishes, and incites violence or hate against groups, based on specific characteristics, such as race, ethnic origin, religious affiliation, political views, physical appearance, gender, etc. [15] The definition points out that HS language incites violence or hatred against groups [16]. There is also an acknowledgment that it is highly probable that HS on social media is related to actual hate crimes. However, there are other kinds of speech whose definition is similar to HS, but are of a different level or effect. One example of these kinds of speech is offensive speech used to hurt someone. The indirect verbal/rhetoric disparity is the key to identifying whether something is hate speech or offensive speech [17]. Social media currently provides localization, which allows users to use different world languages on their websites. One of these languages is Odia, which is one of the oldest spoken and working languages of the Odisha state in India. This language is written from left to right and has its own unique script, which is a syllabic alphabet or an abugida, in which all consonants are embedded with an inherent vowel. Since most of the Indian population are bilingual and multilingual communities, and India is the second largest English-speaking country in the world, it follows that many communities use English-Odia code-mixed languages. The usage of such English-Odia on Facebook is very frequent. An example of the hate texts in English-Odia code-mixed data is given below:  
Hate Text: Sala to rakta peijibi, you don't know me. Translation: You don't know me, I will drink your blood. Substantial research has accomplished the HS detection in English-Hindi code-mixed tweets [18; 19; 20], however, the study of HS detection in English-Odia code-mixed language has not yet been carried out. Nowadays, in India--and especially in Odisha--hate speech based on specific political views, ethnic origin, and religious affiliation has become widespread, and much of this HS calls for violence and attacks on specific targets of individual or groups.