Better approach to assign values to determine potential fake sentences
I'll suggest to test the sentence or the tweet for polarity. This can be done using the textblob library. It can be installed as pip install -U textblob. Once the text data polarity is found, it can be assigned as a separate column in the dataframe. Subsequently, the sentence polarity can then be used for further analysis.
Polarity and Subjectivity are defined as;

Polarity is a float value within the range [-1.0 to 1.0] where 0
indicates neutral, +1 indicates a very positive sentiment and -1
represents a very negative sentiment.
Subjectivity is a float value within the range [0.0 to 1.0] where 0.0
is very objective and 1.0 is very subjective. Subjective sentence
expresses some personal feelings, views, beliefs, opinions,
allegations, desires, beliefs, suspicions, and speculations where as
Objective sentences are factual.

Data
import pandas as pd

# create a dictionary
data = {&quot;Date&quot;:[&quot;1/1/2020&quot;,&quot;2/1/2020&quot;,&quot;3/2/2020&quot;,&quot;4/2/2020&quot;,&quot;5/2/2020&quot;],
    &quot;ID&quot;:[1,2,3,4,5],
    &quot;Tweet&quot;:[&quot;I Hate Migrants&quot;,
             &quot;#trump said he is ok&quot;, &quot;the sky is blue&quot;,
             &quot;the weather is bad&quot;,&quot;i love apples&quot;]}
# convert data to dataframe
df = pd.DataFrame(data)

Notice, the sentiment column is a tuple. So we can split it into two columns like, df1=pd.DataFrame(df['sentiment'].tolist(), index= df.index). Now, we can create a new dataframe to which I'll append the split columns as shown;
df_new = df
df_new['polarity'] = df1['polarity']
df_new.polarity = df1.polarity.astype(float)
df_new['subjectivity'] = df1['subjectivity']
df_new.subjectivity = df1.polarity.astype(float)

Next, on basis of the sentence polarity found earlier, we can now add a label to the dataframe, which will indicate if the tweet/sentence is fake, not fake or neutral.
import numpy as np
conditionList = [
    df_new['polarity'] == 0,
    df_new['polarity'] &gt; 0,
    df_new['polarity'] &lt; 0]
choiceList = ['neutral', 'positive', 'negative']
df_new['label'] = np.select(conditionList, choiceList, default='no_label')
print(df_new)

The result will look like this;
Result
        Date  ID                 Tweet    sentiment  polarity  subjectivity  label
0  1/10/2020   1       I Hate Migrants    (-0.8, 0.9)  -0.8      -0.8        fake
1  2/10/2020   2  #trump said he is ok    (0.5, 0.5)    0.5       0.5        not_fake
2  3/10/2020   3       the sky is blue    (0.0, 0.1)    0.0       0.0        neutral
3  4/10/2020   4    the weather is bad    (-0.68, 0.66) -0.7      -0.7       fake
4  5/10/2020   5         i love apples    (0.5, 0.6)    0.5       0.5        not_fake

Complete Code
import pandas as pd
import numpy as np
from textblob import TextBlob
data = {&quot;Date&quot;:[&quot;1/10/2020&quot;,&quot;2/10/2020&quot;,&quot;3/10/2020&quot;,&quot;4/10/2020&quot;,&quot;5/10/2020&quot;],
        &quot;ID&quot;:[1,2,3,4,5],
        &quot;Tweet&quot;:[&quot;I Hate Migrants&quot;,
                 &quot;#trump said he is ok&quot;, &quot;the sky is blue&quot;,
                 &quot;the weather is bad&quot;,&quot;i love apples&quot;]}
    # convert data to dataframe
df = pd.DataFrame(data)
# print(df)
df['sentiment'] = df['Tweet'].apply(lambda Tweet: TextBlob(Tweet).sentiment)
# print(df)

# split the sentiment column into two
df1=pd.DataFrame(df['sentiment'].tolist(), index= df.index)

# append cols to original dataframe
df_new = df
df_new['polarity'] = df1['polarity']
df_new.polarity = df1.polarity.astype(float)
df_new['subjectivity'] = df1['subjectivity']
df_new.subjectivity = df1.polarity.astype(float)
# print(df_new)

# add label to dataframe based on condition
conditionList = [
    df_new['polarity'] == 0,
    df_new['polarity'] &gt; 0,
    df_new['polarity'] &lt; 0]
choiceList = ['neutral', 'not_fake', 'fake']
df_new['label'] = np.select(conditionList, choiceList, default='no_label')
print(df_new)