1. Breaking News
Exploring Israeli News Bias using Simple Textual Analysis Yuval Pinter Shuki Tausig Oren Persico

2. Motivation / Hypotheses
Media is biased
Israeli media is super-biased
Machine Learning detects bias
Headlines could be enough
"כותרות הן עיתונות בצורתה הצרופה ביותר"
Simon Jenkins, 1992
Which is more significant – class bias or agenda bias?
The idea: classify the news outlet using basic features
Most of the “agenda bias” part will have to wait
No prior work AFAIK; closest field – Authorship attribution

3. github.com/yuvalpinter/MediaAnalysis
Data
General news sites only
Homepage headlines only
Scraped in 15-minute intervals
July 2014 – May 2015
Most experiments on February
Data and extraction code is available:
github.com/yuvalpinter/MediaAnalysis

4. Data Samples
Nov 23, 15:00:
Feb 15, 15:30:

5. Text Processing Consecutive appearance de-duping
Tokenization (inc. lemmatization, affix deletion) using hspell (Har’el and Kenigsberg)
Mostly good, sometimes not so much
הפרלמנט הירדני עמד דקת דומייה לזכר המחבלים =>
פרלמנט ירדן עימד דקה דומייה זכר מחבל
(NRG, 20/11/2014, 0:15)
רעידת אדמה קטלנית כאלף נהרגו בנפאל: "שעות קריטיות"‎ =>
רעידה דימה קטלוניה אילף נהרג נפאל שעה קריטי
(Mako, 25/4/2015, 19:30)

6. Features
Form: character length, word count, word length (average/min/median/max), punctuation token count
Lexicon: quantile word/lemma frequencies
average/min/median/max
Wordlists (Hermit Dave), Israblog (Linzen 2009)
Morphology: affix letters
Word features
Probably the media cycle
Features and extraction code are available

7. Setup & Results 7 classes, 1785 headlines (all of February)
Weka’s Random Forest
Accuracy:
10 trees: 45.4%
50 trees: 49.5%
Most significant features:
Number of words
Average word length
Average position in word frequency table

8. Feature Example
Character length
Character count
Add text descriptions

9. Binary classifier accuracy
Pairwise Setup
Binary classifier accuracy
72.3 88
92.1
73.4
78.5
76.5
84.5
91.8
75.8
78.1
77.9
72.9
86.7 79
79.4
88.9 74
78.2
69.4
64.9
58.6
(Higher = easier to classify = less similar)
Class over agenda:
Mako, Walla, NRG form a cluster – “online ethos”
Ha’aretz and Ma’ariv relatively unique (newspaper-derived)
Israel Hayom resembles tabloid competitor ynet most, more than agenda-sharing NRG

10. Future Work Better content (“agenda”) features
Topic Models?
Sentiment?
Some weird phenomena to be ironed out
Alternating headlines: dedup based on recent k
Very similar headlines: merge or use edit distance
Location-sensitive features
Headlines starting with נתניהו: ~ balanced
Headlines starting with רה"מ: 50% in Israel Hayom, another 25% in NRG
More text: main leads / other headlines

11. github.com/yuvalpinter/MediaAnalysis
Thanks!
github.com/yuvalpinter/MediaAnalysis