Assigning True/False if a token is present in a data-frame

My current data-frame is:

     |articleID | keywords                                               | 
     |:-------- |:------------------------------------------------------:| 
0    |58b61d1d  | ['Second Avenue (Manhattan, NY)']                      |     
1    |58b6393b  | ['Crossword Puzzles']                                  |          
2    |58b6556e  | ['Workplace Hazards and Violations', 'Trump, Donald J']|            
3    |58b657fa  | ['Trump, Donald J', 'Speeches and Statements'].        |  

I want a data-frame similar to the following, where a column is added based on whether a Trump token, ‘Trump, Donald J’ is mentioned in the keywords and if so then it is assigned True :

     |articleID | keywords                                               | trumpMention |
     |:-------- |:------------------------------------------------------:| ------------:|
0    |58b61d1d  | ['Second Avenue (Manhattan, NY)']                      | False        |      
1    |58b6393b  | ['Crossword Puzzles']                                  | False        |          
2    |58b6556e  | ['Workplace Hazards and Violations', 'Trump, Donald J']| True         |           
3    |58b657fa  | ['Trump, Donald J', 'Speeches and Statements'].        | True         |       

I have tried multiple ways using df functions. But cannot achieve my wanted results. Some of the ways I’ve tried are:

df['trumpMention'] = np.where(any(df['keywords']) == 'Trump, Donald J', True, False) 

or

df['trumpMention'] = df['keywords'].apply(lambda x: any(token == 'Trump, Donald J') for token in x) 

or

lst = ['Trump, Donald J']  
df['trumpMention'] = df['keywords'].apply(lambda x: ([ True for token in x if any(token in lst)]))   

Raw input:

df = pd.DataFrame({'articleID': ['58b61d1d', '58b6393b', '58b6556e', '58b657fa'],
                   'keywords': [['Second Avenue (Manhattan, NY)'],
                                ['Crossword Puzzles'],
                                ['Workplace Hazards and Violations', 'Trump, Donald J'],
                                ['Trump, Donald J', 'Speeches and Statements']],
                   'trumpMention': [False, False, True, True]})

>Solution :

try

df["trumpMention"] = df["keywords"].apply(lambda x: "Trump, Donald J" in x)