## 4 Results  
### Exploratory Data Analysis (EDA)  
In this part, we analyze the data by examining the text length distribution, frequency of unique words and n-grams across different categories of sexist texts, and sentiment analysis of common word pairs within each category. We aim to uncover linguistic patterns and characteristics that distinguish sexist texts from non-sexist ones. First, we looked at the relationship between text length and the presence of sexist content. We calculated the text length based on the number of characters in each text, after removing stop words, punctuation, etc. We divided the text length into five ranges, [2-50, 51-100, 101-150, 151-200, 201+], to simplify the analysis. A density distribution of text lengths within each label category (sexist or non-sexist) is shown in Figure 4.  
We used several statistical methods, including logistic regression, chi-square tests, and \(t\)-tests, to analyze how text length influences sexist labeling. Table 3 show the results from the logistic regression, and Table 4 show the results from the l chi-square tests, and \(t\)-tests show that longer texts are more likely to be labelled as sexist; however, the logistic regression results indicate a low R-squared value, near 0.004, suggesting that text length alone is not a strong predictor. More details are shown in Tables 3 and 4:  
Based on the results above, with a \(p\)-value of \(4.98\times 10^{-14}\), \(1.83\times 10^{-13}\), and \(4.21\times 10^{-14}\) for logistic regression, chi-square test, and t-test, respectively, it is evident that text length is a significant factor in determining whether a text is labeled as sexist. All three tests consistently indicate a strong and statistically significant relationship between text length and sexist labeling. \begin{table}
\begin{tabular}{l l} \hline \hline
**Statistic** & **Value** \\ \hline Chi2 Statistic & 65.703 \\ \(p\)-Value & 1.83 \(\times\)\(10^{-13}\) \\ Degrees of Freedom & 4 \\ \hline T-Statistic & 7.562 \\ \(p\)-Value & 4.21 \(\times\)\(10^{-14}\) \\ Mean Text Length (Sexist) & 64.83 \\ Mean Text Length (Non-Sexist) & 58.29 \\ Standard Deviation (Sexist) & 51.41 \\ Standard Deviation (Non-Sexist) & 50.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chi-Square and T-test results. Figure 4: Density distribution of text lengths by sexist label. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Parameter** & **Coefficient** & **Standard Error** & **z-Value** & \(p\)**-Value** & **95\% Confidence Interval** \\ \hline Intercept & \(-1.431497\) & 0.044 & \(-32.262\) & \(2.37\times 10^{-228}\) & \([-1.518,-1.345]\) \\ Text Length & 0.003579 & 0.000 & 7.532 & 4.98 \(\times 10^{-14}\) & \([0.003,0.005]\) \\ Log-Likelihood: \(-7730.043\) & & & & & \\ R-Squared: 0.003650 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Logistic regression results. To further analyze the linguistic features of sexist content, we intend to understand the typical language used in it. We implemented n-gram analysis to identify common bigrams and unique words in sexist texts. Figures 5 and 6 show the top 20 unique words and bigrams in sexist texts. As we can see in Figures 5 and 6, the word 'women' was the most frequently occurring word in sexist texts, highlighting the targeted nature of these texts. We also notice that some words indicate aggressive and derogatory language. The sexist text is categorized into 4 main categories including 'threats, plans to harm, and incitement', 'derogation', 'animosity', and 'prejudiced discussions' by Task B. Figure 7 demonstrates the proportions of different types of sexist content. As we can see, 'derogation' was the most prevalent, comprising 46.8% of the sexist texts. In Figure 8, we identify the top unique words for each category of sexist content to understand common words associated with each category. In category 1, common words included 'women', 'beat', and'shit', reflecting violent and harmful intentions.