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Immigration-themed children’s literature can be an important resource in the classroom, especially because some U.S. immigrant groups, including French-Canadians, have received limited curricular representation. Using the qualitative method of critical content analysis, this study aims to examine depictions of French-Canadian immigrants to the United States in contemporary children’s books.

Postcolonialism is employed as an analytical lens with special attention given to the ways immigrant characters are constructed as different from the dominant group (i.e., othering), how dominant group values are imposed on immigrant characters, and how immigrant characters resist othering and domination. Three books comprise the sample: “Charlotte Bakeman Has Her Say” by Mary Finger and illustrated by Kimberly Batti, “Other Bells for Us to Ring” by Robert Cormier, and “Red River Girl” by Norma Sommerdorf.

The findings reveal multiple instances in which French-Canadian immigrants are constructed as Other and few instances in which these characters resist this positioning, and these books reflect the real ways French-Canadians were perceived as subalterns during the mass migration from Québec to the United States between the late 19th and early 20th centuries.

This study is significant because it examines portrayals of a substantial immigrant group that has been overlooked in the immigration history curriculum. This sample of children’s books may be used to teach children the complexities of immigration history and provide a more nuanced understanding of immigration during the 19th and 20th centuries.

This study aims to investigate to what extent the predictability of the standard and poor’s 500 (S&P 500) price levels is enhanced by investors’ sentiments extracted from social media content, specifically platform X.

Two recurrent neural network (RNN) models are developed. The first RNN model is merely based on historical records and technical indicators. In addition to the variables included in the first RNN model, the second RNN model comprises the outputs of the sentiment analysis, performed using the TextBlob library. The study was conducted between December 28, 2011, and December 30, 2021, over 10 years, to obtain better results by feeding the RNN models with a significant quantity of data by extending the period and capturing an extensive timespan.

Comparing the performance of both models reveals that the second model, with sentiment analysis inputs, yields superior outcomes. The mean absolute error (MAE) of the second model registered 72.44, approximately 50% lower than the MAE of the technical model, its percentage value, the mean absolute percentage error, recorded 2.16%, and finally, the median absolute percentage error reached a value of 1.30%. This underscores the significant influence of digital platforms in influencing the behavior of certain assets like the S&P 500, emphasizing the relevance of sentiment analysis from social media in financial forecasting.

This study contributes to the growing body of literature by highlighting the enhanced predictive power of deep learning models that incorporate investor sentiment from social media, thereby advancing the application of behavioral finance in financial forecasting.