delab-trees 0.4.2

a library to analyse reply trees in forums and social media

Delab Trees

A library to analyze conversation trees.

Installation

pip install delab_trees

Get started

Example data for Reddit and Twitter are available here https://github.com/juliandehne/delab-trees/raw/main/delab_trees/data/dataset_[reddit|twitter]_no_text.pkl.

The data is structure only. Ids, text, links, or other information that would break confidentiality of the academic

access have been omitted.

The trees are loaded from tables like this:

| | tree_id | post_id | parent_id | author_id | text | created_at |

|---:|----------:|----------:|------------:|:------------|:------------|:--------------------|

| 0 | 1 | 1 | nan | james | I am James | 2017-01-01 01:00:00 |

| 1 | 1 | 2 | 1 | mark | I am Mark | 2017-01-01 02:00:00 |

| 2 | 1 | 3 | 2 | steven | I am Steven | 2017-01-01 03:00:00 |

| 3 | 1 | 4 | 1 | john | I am John | 2017-01-01 04:00:00 |

| 4 | 2 | 1 | nan | james | I am James | 2017-01-01 01:00:00 |

| 5 | 2 | 2 | 1 | mark | I am Mark | 2017-01-01 02:00:00 |

| 6 | 2 | 3 | 2 | steven | I am Steven | 2017-01-01 03:00:00 |

| 7 | 2 | 4 | 3 | john | I am John | 2017-01-01 04:00:00 |

This dataset contains two conversational trees with four posts each.

Currently, you need to import conversational tables as a pandas dataframe like this:

import pandas as pd

from delab_trees import TreeManager



d = {'tree_id': [1] * 4,

     'post_id': [1, 2, 3, 4],

     'parent_id': [None, 1, 2, 1],

     'author_id': ["james", "mark", "steven", "john"],

     'text': ["I am James", "I am Mark", " I am Steven", "I am John"],

     "created_at": [pd.Timestamp('2017-01-01T01'),

                    pd.Timestamp('2017-01-01T02'),

                    pd.Timestamp('2017-01-01T03'),

                    pd.Timestamp('2017-01-01T04')]}

df = pd.DataFrame(data=d)

manager = TreeManager(df) 

# creates one tree

test_tree = manager.random()

Note that the tree structure is based on the parent_id matching another rows post_id.

You can now analyze the reply trees basic metrics:

from delab_trees.main import get_test_tree

from delab_trees.delab_tree import DelabTree



test_tree : DelabTree = get_test_tree()

assert test_tree.total_number_of_posts() == 4

assert test_tree.average_branching_factor() > 0

A summary of basic metrics can be attained by calling

from delab_trees.test_data_manager import get_test_tree

from delab_trees.delab_tree import DelabTree



test_tree : DelabTree = get_test_tree()

print(test_tree.get_author_metrics())



# >>> removed [] and changed {} (merging subsequent posts of the same author)

# >>>{'james': <delab_trees.delab_author_metric.AuthorMetric object at 0x7fa9c5496110>, 'steven': <delab_trees.delab_author_metric.AuthorMetric object at 0x7fa9c5497dc0>, 'john': <delab_trees.delab_author_metric.AuthorMetric object at 0x7fa9c5497a00>, 'mark': <delab_trees.delab_author_metric.AuthorMetric object at 0x7fa9c5497bb0>}

More complex metrics that use the full dataset for training can be gotten by the manager:

import pandas as pd

from delab_trees import TreeManager



d = {'tree_id': [1] * 4,

     'post_id': [1, 2, 3, 4],

     'parent_id': [None, 1, 2, 1],

     'author_id': ["james", "mark", "steven", "john"],

     'text': ["I am James", "I am Mark", " I am Steven", "I am John"],

     "created_at": [pd.Timestamp('2017-01-01T01'),

                    pd.Timestamp('2017-01-01T02'),

                    pd.Timestamp('2017-01-01T03'),

                    pd.Timestamp('2017-01-01T04')]}

df = pd.DataFrame(data=d)

manager = TreeManager(df) # creates one tree

rb_vision_dictionary : dict["tree_id", dict["author_id", "vision_metric"]] = manager.get_rb_vision()

The following two complex metrics are implemented:

from delab_trees.test_data_manager import get_test_manager



manager = get_test_manager()

rb_vision_dictionary = manager.get_rb_vision() # predict an author having seen a post

pb_vision_dictionary = manager.get_pb_vision() # predict an author to write the next post

How to cite

    @article{dehne_dtrees_23,

    author    = {Dehne, Julian},

    title     = {Delab-Trees: measuring deliberation in online conversations},        

    url = {https://github.com/juliandehne/delab-trees}     

    year      = {2023},

}