Based on the hint provided and the analysis of the dataset, here are identified issues related to the mismatch between column names and descriptions:

1. Issue with column name: **vote_count**
    - **Issue**: "Misleading column name compared to content"
    - **Evidence**: "vote_count(Date of publish)"
    - **Description**: The column name suggests it should contain the number of votes a movie has received, but the description in brackets indicates it might represent the "Date of publish," which is mismatching. The column name and its content description do not align, leading to confusion about the data it actually represents.

2. Issue with column name: **vote_average**
    - **Issue**: "Misleading column description compared to column name"
    - **Evidence**: "vote_average(popularity)"
    - **Description**: The column name `vote_average` suggests it should contain numeric values indicating the average votes a movie received, yet the description in brackets labels it as "popularity," which commonly refers to how well-liked or recognized something is, not its average vote score. This mismatch between the column's name and its description might cause misunderstanding of the data type and content.

3. Issue with column names: **Multiple Columns**
    - **Issue**: "Inconsistent naming convention and description"
    - **Evidence**: "title(Movie name)", "overview(A small summary of the movie)", "original_language(Language of shooting)"
    - **Description**: The dataset columns use a naming convention that pairs technical terms with layman descriptions in parentheses, which might be confusing or unnecessary. For example, "title(Movie name)" could simply be "Title" or "Movie Name", and "original_language(Language of shooting)" could be more accurately described as "Original Language" if it refers to the language in which the movie was shot. This format leads to ambiguity in understanding whether descriptions are clarifications or corrections of the column names.