Response Distributions

Lets look at the answer distributions for each of the 3 questions in out survey.



In [19]:

    
%load_ext autoreload
%autoreload 2
%matplotlib inline

import inspect, os
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
os.sys.path.insert(0,parentdir) 
from data_generation.join_traces_and_survey import load_survey_dfs
from response_distributions_util import *
import copy









    



The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

Load and Prep Data



In [9]:

    
d_survey, d_joined = load_survey_dfs()
d_desktop = d_joined[d_joined['host'] == 'mobile']
d_mobile = d_joined[d_joined['host'] == 'desktop']
d_single_motivation = d_joined[d_joined['motivation'].apply(lambda x: len(x.split('|')) == 1)]

print('Num Responses: ', d_survey.shape[0])
print('Num in EL: ', d_joined.shape[0])
print('Num Mobile Responses in EL: ', d_desktop.shape[0])
print('Num Desktop Responses in EL: ', d_mobile.shape[0])









    



Num Responses:  35948
Num in EL:  32792
Num Mobile Responses in EL:  15059
Num Desktop Responses in EL:  17733

Q1 Information Depth

I am reading this article to ... [Information Depth]

look up a specific fact or to get a quick answer. [fact]
get an overview of the topic. [overview]
get an in-depth understanding of the topic. [in-depth]

Information Depth Histogram (Dektop vs Mobile)



In [31]:

    
x = 'information depth'
hue = 'host'
title = 'Mobile vs Desktop Information Depth Distribution'
xorder  = ['in-depth', 'overview', 'fact']
plot_proportion(d_joined, x, hue, title,  xorder = xorder)

The distributions are relatively similar across mobile and desktop. The most common use case is to get an overview, then look up a fact, then get an in depth understanding.

Q2 Familiarity

Prior to visiting this article ... [Prior Knowledge]

I was already familiar with the topic. [familiar]
I was not familiar with the topic and I am learning about it for the first time. [unfamiliar]

Prior Knowledge Histogram (Dektop vs Mobile)



In [32]:

    
x = 'prior knowledge'
hue = 'host'
title = 'Mobile vs Desktop'
plot_proportion(d_joined, x, hue, title)

Desktop and mobile are basically identical. People are slightly more likely to be familiar with the topic they are reading about.

Q3 Motivation

I am reading this article because ... [Motivation]

I have a work or school-related assignment. [work/school]
I need to make a personal decision based on this topic (e.g., to buy a book or game, to choose a travel destination). [personal-decision]
I want to know more about a current event (e.g. Black Friday, a soccer game, a recent earthquake, somebody's death). [current event]
the topic was referenced in a piece of media (e.g. TV, radio, article, film, book). [media]
the topic came up in a conversation. [conversation]
I am bored or randomly exploring Wikipedia for fun. [bored/random]
this topic is important to me and I want to learn more about it. (e.g., to learn about a culture). [intrinsic_learning]

Number of Motivations Distribution

Subjects were allowed to select multiple reasons. How many motivations do people select?



In [34]:

    
d_in = pd.DataFrame()
d_in['counts'] = d_joined['motivation'].apply(lambda x: len(x.split('|'))).value_counts()
d_in['Proportion'] = d_in['counts'] / d_in['counts'].sum()
d_in['# of Reasons Given'] = d_in.index

fig = sns.barplot(y="Proportion",
            x = '# of Reasons Given',
            data=d_in,
            color = (0.54308344686732579, 0.73391773700714114, 0.85931565621319939)
            )

30% of respondents listed more than one motivation.

Single Motivation Histogram

For responses with only a single motivation, what is the distribution over motivations.



In [36]:

    
d_in = pd.DataFrame()
d_in['counts'] = d_single_motivation['motivation'].value_counts()
d_in['proportion'] = d_in['counts'] / d_in['counts'].sum()
d_in['motivation'] = d_in.index

fig = sns.barplot(y="proportion",
            x = 'motivation',
            data=d_in,
            color = (0.54308344686732579, 0.73391773700714114, 0.85931565621319939),
            )
plt.ylabel('Proportion')
plt.xlabel('Motivation')

for item in fig.get_xticklabels():
    item.set_rotation(45)

Media and work/school are the most popular

Single Motivation Histogram (Dektop vs Mobile)



In [37]:

    
x = 'motivation'
hue = 'host'
title = 'Mobile vs Desktop'
order = ['media', 'work/school','intrinsic learning', 'bored/random', 'conversation', 'other','current event', 'personal decision', ]
plot_proportion(d_single_motivation, x, hue, title, xorder = order, rotate=True)

For Desktop, the most common motivation is work/school. For Mobile, it is media. Also, for mobile users, conversation is more likely compared to desktop.

Motivation Histogram

For each motivation lets count how often it was chosen as at least one of the motivations.



In [44]:

    
d_in = pd.DataFrame(columns = ['motivation', 'counts'])
ms = [
        'work/school',
        'personal decision',
        'current event',
        'media',"conversation",
        'bored/random',
        'no response',
        'intrinsic learning',
        'other'
    ]

for i, m in enumerate(ms):
     d_in.loc[i] = [m, d_joined['motivation'].apply(lambda x: m in x).sum()]


d_in['proportion'] = d_in['counts'] / d_in['counts'].sum()
d_in.sort_values(by = 'counts', inplace = True, ascending = False)
fig = sns.barplot(y="proportion",
            x = 'motivation',
            data=d_in,
            color = (0.54308344686732579, 0.73391773700714114, 0.85931565621319939),
            )
plt.ylabel('Proportion')
plt.xlabel('Motivation')

for item in fig.get_xticklabels():
    item.set_rotation(45)

Suddenly intrinsic learning features much more prominently. It must be a common occurence in multi-choice answers.

Double Motivation Co-occurrence Heatmaps

For users who chose 2 motivations, which motivations co-occur?



In [45]:

    
df = copy.deepcopy(d_joined[d_joined['motivation'].apply(lambda x: len(x.split('|')) == 2)])
df['pm'] = df['motivation'].apply(lambda x: '|'.join(sorted(x.split('|'))))
df_joint = pd.DataFrame()
df_joint['count'] = df['pm'].value_counts()
df_joint['pm'] = df_joint.index
df_joint.index = range(0, df_joint.shape[0])
df_joint['m1'] = df_joint['pm'].apply(lambda x: x.split('|')[0])
df_joint['m2'] = df_joint['pm'].apply(lambda x: x.split('|')[1])
df_joint['count'] = df_joint['count'].apply(int)



In [46]:

    
df_joint2 = copy.deepcopy(df_joint)
df_joint2['pm'] = df_joint2['pm'].apply(lambda x: '|'.join(sorted(x.split('|'), reverse = True)))
df_joint2['m1'] = df_joint2['pm'].apply(lambda x: x.split('|')[0])
df_joint2['m2'] = df_joint2['pm'].apply(lambda x: x.split('|')[1])
df_joint2.index = range(df_joint.shape[0], 2 * df_joint.shape[0])



In [47]:

    
df_joint12 = pd.concat([df_joint, df_joint2]).pivot("m1", "m2", "count")
#ax = sns.heatmap(df_joint12, annot=True, fmt="0.0f")
#plt.ylabel('Motivation 1')
#plt.xlabel('Motivation 2')
#plt.title('Raw Co-occurence counts')

The since some motivations are more popular than others, the color coding can be misleading. Let's look at the conditional distributions instead.



In [48]:

    
df_joint12_norm = df_joint12.div(df_joint12.sum(axis=1), axis=0)
ax = sns.heatmap(df_joint12_norm, annot=True, fmt="0.2f")
plt.ylabel('Motivation 1')
plt.xlabel('P(Motivation 2 | Motivation 1)')
plt.title('Conditional Distributions')









    Out[48]:





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Given that work/school is a motivation, the most common other motivation is intrinsic_learning by a long shot. seems like people in our survey who choose 2 motivations like their job/studies
the pattern is similar for personal decisions
Given that people are bored/randomly exploring, their most likely other motivations is media. the next most likely is intrinsic_learning
the pattern is similar for current events

Response Co-occurence

Information Depth and Prior Knowledge



In [49]:

    
x = 'information depth'
hue = 'prior knowledge'
title = 'P(Prior Knowledge | Information Depth = x) '
xorder = order = ['in-depth', 'overview', 'fact']
plot_proportion(d_joined, x, hue, title,  xorder = xorder, normx=False)

When seeking in-depth information or looking up a fact, readers are more likely to be familiar with the topic. When they are seeking an overview, the are more likely to be unfamiliar.



In [50]:

    
hue = 'information depth'
x = 'prior knowledge'
title = 'P(Information Depth | Prior Knowledge = x)'
xorder = order = ['familiar', 'unfamiliar']
plot_proportion(d_joined, x, hue, title,  xorder = xorder, normx=False)

Readers famailiar with the topic are most likely to be looking up a fact. Unfamiliar users are the most likely to be getting an overview.

Prior Knowledge and Motivation



In [51]:

    
hue = 'prior knowledge'
x = 'motivation'
title = 'P(Prior Knowledge | Motivation = x)'
plot_proportion(d_single_motivation, x, hue, title, rotate=True, normx=False)

When people come for intrinsic learning, they tend to be familair with the topic already. When people come because of a reference in the media, they tend to be unfamialiar with the topic.



In [52]:

    
x = 'prior knowledge'
hue = 'motivation'
title = 'P(Motivation | Prior Knowledge = x)'
plot_proportion(d_single_motivation, x, hue, title, rotate=True, normx=False)

Bad Visualization

Information Depth and Motivation



In [53]:

    
hue = 'information depth'
x = 'motivation'
title = 'P(Information Depth | Motivation = x)'
plot_proportion(d_single_motivation, x, hue, title, rotate=True, normx=False)

bored/random users are interested in getting an overview. Users in a conversation are loking up a fact...



In [54]:

    
x = 'information depth'
hue = 'motivation'
title = 'P(Motivation | Information Depth  = x)'
plot_proportion(d_single_motivation, x, hue, title, rotate=True, normx=False)



In [ ]:

    
Bad Visualization