This study examines the effectiveness of using YouTube videos in teaching the speaking skills among English as a Foreign Language (EFL) students in Jordan. The study sample comprised 80 students attending Oral Skills classes in the English Language and Literature Department at a private university in Jordan. The participants were equally divided into a control group and an experimental group of 40 students each. The experimental group was taught through the use of YouTube videos, while the control group was taught the speaking skills using the traditional approach. A pre-test and a post-test were administered to the two groups. Four TEFL experts were asked to rate the participants’ performance using the IELTS speaking band descriptors, which consist of four main categories: fluency & coherence, lexical resource, grammatical range & accuracy, and pronunciation. The findings showed that the performance of the two groups was improved. However, compared to the traditional group, the experimental group demonstrated a relatively better improvement. The results also showed significant progress in the speaking performance of the students subjected to the YouTube experiment. Of all the four constructs under investigation, pronunciation and fluency & coherence were the most noticeably advanced in the performance of the YouTube experimental group. The present study recommends that YouTube videos be embedded into the EFL classroom to improve students' speaking skills.

This study adopts a corpus-assisted approach to explore the translation strategies that Netflix subtitlers opted for in rendering 1564 English swear words into Arabic. It uses a 699,229-word English-Arabic parallel corpus consisting of the English transcriptions of forty English movies, drama, action, science fiction (sci-fi), and biography and their Arabic subtitles. Using the wordlist tool in SketchEngine, the researchers identified some frequent swear words, namelyfuck, shit, damn, ass, bitch, bastard, asshole, dick, cunt, andpussy. Moreover, using the parallel concordance tool in SketchEngine revealed that three translation strategies were observed in the corpus, namely, omission, softening, and swear-to-non-swear. The omission strategy accounted for the lion’s share in the investigated data, with 66% for drama, 61% for action, 52% for biography, and 40% for sci-fi. On the other hand, the swear-to-non-swear strategy was the least adopted one, accounting for 21% in sci-fi, 16% in biography, 14% in drama, and 11% in action. In addition, the softening strategy got the second-highest frequency across the different movie genres, with 39% for sci-fi, 32% for biography, 28% for action, and 20% for drama. Since swear words have connotative functions, omitting or euphemizing them could cause a slight change in the representation of meaning and characters. The study recommends more corpus-assisted studies on different AVT modes, including dubbing, voiceover, and free commentaries.

The surge of new words and phrases accompanying the sudden COVID-19 outbreak has created new lexical and sociolinguistic changes that have become part of our lives. The emergence of COVID-19s coinages has remarkably increased to establish a trending base of global neologisms. The present study attempts to investigate the nature of the new English words and expressions that emerged in the wake of the COVID-19 crisis. It also identifies the type of word-formation processes that contributed to the emergence of these neologisms in the English language. The researchers compiled a corpus of 208 COVID-19-inspired neologisms from different sources, including social networking websites, search engines, blogs, and news articles. The analysis revealed that word-formation processes were so varied to cover all possible forms of derivation, including affixation, compounding, blending, clipping, acronyms, among others, along with dual word-formation processes, with compounding and blending being the most discrete. The findings showed that the flux of new terms demonstrates the creativity and vitality of the English language to respond to emerging situations in times of crisis. The study recommends that further research be carried out on the new terms that have been transferred to other languages as loanwords, loan-translations and loan-blends.

Critical Discourse Analysis (CDA) investigates the relationship between language, power, and society. Corpus linguistics (CL) is the study of language based on examples of real life language use. Over the last two decades, various scholars have combined some approaches and notions of CDA with the analytical framework of CL to examine the representation of several phenomena in relatively large texts. This study follows a corpus-assisted (critical) discourse analysis approach to investigate a 2.5 million word corpus of Arabic news articles by Jordan’s News Agency (PETRA). It demonstrates how some researchers following this approach may make some decisions, at some stages of their analysis, which are likely to affect their findings. These potential decisions may include selecting what statistical measures to use, what threshold to consider, what terms from the frequency, cluster, and collocation results to further investigate, which concordance lines to include in their study, and some others. In this study, I argue that some of these decisions can be made to suit the researchers’ preconceived assumptions and pre-existing hypotheses. The study concludes that using corpus linguistic techniques to discursively analyze large data reduces but not completely removes researchers’ bias.