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Fake News Challenge

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The Netizens team is a student led hacking team, a group part of the University of Hertfordshire that has many cyber security projects. These projects come in many forms, including hardware, software, procedures and much more. One of these interests has been the Fake News Challenge, or FNC as we have come to refer to it.

Why Fake News?

Dodge Ball

Our thinking is, “if you can detect fake news, you can detect malicious social hacking”. This could be in the form of phishing emails, scamming websites or malicious programs. The ability to detect human deception attempts allows for a whole class of systems that detect users. Bringing artificial intelligence to these systems allows for smarter detection.



Fake news, as defined on the FNC site, by the New York Times:

0001 Fake news, defined by the New York Times as “a made-up story with an
0002 intention to deceive” 1, often for a secondary gain, is arguably one of the
0003 most serious challenges facing the news industry today. In a December Pew
0004 Research poll, 64% of US adults said that “made-up news” has caused a
0005 “great deal of confusion” about the facts of current events 2.

The goal of the competition is to explore how modern techniques in machine learning and artificial intelligence can be used to fact check news sources. Doing so is considered if a time consuming and often difficult task for experts, but automating the process with stance detection allows for fast classification of news sources if accurate.

More can be found here.

Problem Formalisation

The formalism of the stance classification is defined as follows:

0006 Input
0007   A headline and a body text - either from the same news article or from
0008   two different articles.
0009 Output
0010   Classify the stance of the body text relative to the claim made in the
0011   headline into one of four categories:
0012     1. Agrees: The body text agrees with the headline.
0013     2. Disagrees: The body text disagrees with the headline.
0014     3. Discusses: The body text discuss the same topic as the headline, but
0015     does not take a position
0016     4. Unrelated: The body text discusses a different topic than the
0017     headline

The scoring is done as follows:

FNC Evaluation

There are several rules associated with the competition:

  • Only use the provided data.
  • Winning teams must open source their solutions with the Apache 2.0 licence.
  • Have fun.


Initial Thoughts

Our initial design thinking was to produce a framework that would allow us to attempt several different ideas, having the data input and data output all handled by the program. The next line of thinking was to have the program automated via the terminal, so that we could test multiple implementations over some long period of time and come back and analyse the results.

This was done is Java, as it was considered an easy language to implement this in and allows for debugging. Our team members also use multiple different development OSes, so we decided to use a language that works on most. In hindsight, a data orientated (such as R or Python) would have been more appropriate - live and learn.

For our initial design, we implemented 1st nearest neighbour. This gave us very poor results, not matching anything other than one label.

Due to implementation, running the program was taking 10 hours, for single nearest neighbour! We stored our database in RAM as strings and casted to numeric values each time - that was incredibly slow. After realising our mistake, we got our timing down to about a minute for analysing and matching - much better!


We later found a few more problems in our coded:

  • The classification wasn’t working correctly
  • The code doesn’t easily extend to multiple nearest neighbours

A re-write soon sorted this and were able to validate that our code was indeed working as intended. Impressively, even for K values larger than 100, we were still able to classify our data in the order of minutes.


Source Code

Please refer to our snapshot repository for the source code for our version of the software for the competition.

TODO: Discuss the code layout.

The code can be visualised as follows (source):

As you’ll see, everything has been completely written from scratch - not a single line has been borrowed from somewhere. If anything, this makes this project impressive within it’s own right.

Code Overview

The following is a description of each class and it’s purpose:

  • - Interface - Define how classifiers are represented in the program.
  • - The basic database handler for the CSV files.
  • - The basic dataset structure handling a combination of the titles and bodies.
  • - Fixes the output file to be competition ready.
  • - Defines the K-nearest neighbours algorithm for classification.
  • - The main entry into the program, responsible for handling the command line parameters and starting the program logic.
  • - Calculates the entropy of the body data.
  • - Calculates the entropy of the head data.
  • - Interface - Define how markers are represented in the program.
  • - Calculates the number of matching words for the body and a dictionary.
  • - Calculates the number of matching words for the head and a dictionary.
  • - Calculates the number of words in the body data.
  • - Calculates the number of words in the head data.
  • - Calculates the number of words that match in the head and body.
  • - Calculates the number of words ratio between the head and body.
  • - Checks the result of the scoring process.

Further comments exist in the source files themselves.

Break Down


Running the following:

0018 ant; java -jar fnc.jar -h


0019 ./fnc.jar [OPT]
0021   OPTions
0023     -c  --clss    Classified data
0024       <FILE>   CSV titles file
0025       <FILE>   CSV bodies file
0026     -h  --help    Displays this help
0027     -k  --kner    Set the K for k-nearest
0028       <INT>    Number of nearest
0029     -j  --jobs    Set the number of jobs to run
0030       <INT>    Number of jobs
0031     -m  --mode    Set the mode
0032       knear    (def) k-nearest neighbours
0033     -r  --runs    Set the number of runs
0034       <INT>    Number of runs
0035     -u  --ucls    Unclassified data
0036       <FILE>   CSV titles file
0037       <FILE>   CSV bodies file
0038     -f  --fix     Fixes the results to it's original form.
0039       <FILE>      The original whole titles database

The options are self explanatory, but of interest to us is in particular is the K value, which allows us to define how many nearest neighbours we take into consideration. In the results section, we see the outcome of varying our K value.


The following is the interface (without comments):

0040 public interface Marker{
0041   public String getName();
0042   public double analyse(DataSet ds, int y);
0043 }

As you can see, it’s very simple. Each of the makers is given a reference name, as well as accepting the data to analyse at a given position and then returning a double value associated with it. This double value can be the full range of double, where it is normalised after.

We have the following types of markers:

  • Number of Words - This is the number of words given in each set. The thinking here is that longer articles would require a higher time investment, so people may be likely to write shorter articles if they are producing fake news.
  • Ratio of Number of Words - This is the ratio of how long the head is compared to the body. This line of thought was for the possibility of having a disproportionally long head or body when compared to their counter. Fake news might be a harder sell and therefore a longer title or real news may have richer content, therefore longer titles.
  • Number of Words Match - This is the number of matching words. Obviously, if a title talks anything about the body, you would hope they have matching words somewhere.
  • Dictionary Match - Match the top 100 words with an quadratic back off. The more used the word, the higher the rating. The thinking here was that a person writing fake news may not have the greatest vocabulary, or a good writer may use common words to make articles more readable.
  • Entropy - A calculation of the entropy of the words, again people writing fake news may be tempted to repeat their sentences. Equally, somebody writing a well written article may repeat points for clarity.

NOTE: We actually ended up turning off the dictionary match marker, as this actually made the matching process harder.


Our Results

During our initial testing, we were getting results above 85% when classifying 80% of the training data, using 20% of the training data to train.

Varying K Value

Varying the K value had a large affect and there was clearly an advantage to looking at about 105 of a node’s nearest neighbours for the purpose of classification.

Our actual results were 83.0% using the new test data, trained with the previously released training data. This is considerably lower than our original classification, but this is understandable given the amount of overlap in our previous data training data.

Competition Results

One additional restraint added at the end of the competition was that 1 hour before closure, at 23:59 GMT on the 2nd of June, the results table would go “dark” in order to add some additional mystery to who the winner is.

Below we have the results table, pulled at 22:56 GMT:

0044 submission_pk User             score
0045 404231        seanbaird        9556.500 (1)
0046 404214        athene           9550.750 (2)
0047 404189        jaminriedel      9521.500 (3)
0048 404185        shangjingbo_uiuc 9345.500 (4)
0049 404142        enoriega87       9289.500 (5)
0050 404228        OSUfnc2017       9280.500 (6)
0051 404205        florianmai       9276.500 (7)
0052 404197        humeaum          9271.500 (8)
0053 403966        pebo01           9270.250 (9)
0054 404194        gtri             9243.000 (10)
0055 404226        jamesthorne      9189.750 (11)
0056 404133        Soyaholic        9146.250 (12)
0057 403872        ezequiels        9117.500 (13)
0058 404233        Tsang            9111.500 (14)
0059 404129        siliang          9103.750 (15)
0060 404062        neelvr           9090.750 (16)
0061 404080        stefanieqiu      9083.250 (17)
0062 404215        htanev           9081.250 (18)
0063 404221        tagucci          9067.000 (19)
0064 404070        saradhix         9043.500 (20)
0065 404137        acp16sz          8995.750 (21)
0066 404176        Gavin            8969.750 (22)
0067 404092        JAIST            8879.750 (23)
0068 404114        subbareddy       8855.000 (24)
0069 404025        amblock          8797.500 (25)
0070 404036        annaseg          8797.500 (25)
0071 403978        Debanjan         8775.250 (26)
0072 404183        johnnytorres83   8761.750 (27)
0073 404086        avineshpvs       8539.500 (28)
0074 403858        bnns             8349.750 (29)
0075 404108        gayathrir        8322.750 (30)
0076 404076        MBAN             8321.000 (31)
0077 404209        martineb         8273.750 (32)
0078 403809        contentcheck     8265.750 (33)
0079 404204        vivek.datla      8125.750 (34)
0080 404227        vikasy           7833.500 (35)
0081 404175        barray           7778.250 (36)
0082 404232        MicroMappers     7420.250 (37)
0083 404082        borisnadion      7160.500 (38)
0084 403913        etrrosenman      7106.750 (39)
0085 404033        agrena           6909.750 (40)
0086 404056        gpitsilis        6511.750 (41)
0087 404224        mivenskaya       6494.750 (42)
0088 403851        rldavis          5617.500 (43)
0089 404065        isabelle.ingato  5298.500 (44)
0090 404049        daysm            4911.000 (45)
0091 404184        mrkhartmann4     4588.750 (46)
0092 404106        bertslike        4506.500 (47)
Number of Entries

All-in-all, it seems as though we had 50 entries in total - so by extension some people entered when the score board finally went dark. Given our position in the table, if they randomly placed it is likely they placed above us. This means we placed a respectable 36-39 out of 50.


So now, we look at how things have gone in reflection.

Thinking about the implementation, I think we’re mostly happy to have travelled the road we have in terms of the learning experience involved with writing everything from scratch. Of course, given the opportunity again, I think we would possibly explore the idea of the using a higher level system for analysing the data, possibly as several different tools rather than one large rolled tool.

This also comes down to the classic “given more time”. Both the people on our team were extremely pressed for time with various happenings, both of us leaving the UK for other Countries, as well as many other University related pressures. This project really only saw a day perhaps in a month - so it’s impressive it got this far.

Speaking of being impressed, it’s surprising how far you can get with just a k-nearest neighbour algorithm. It’s certainly fast! You could very easily label data on the fly, possibly even in browser per client if you were using this as a web tool.

We didn’t come last! Given our limited time, our long path to solving the problem, miraculously we didn’t even come last in solving the problem. 83.0% classification of some completely unknown data is really not too bad at all.

The competition itself was run very nicely, given the number of people competing from various Countries and time zones. Some things could have been done better, but considering this was their first attempt and they were not sure how successful it would be - I think this is an impressive accomplishment to those involved. Hats off to them.

Lastly, this competition is called “FNC-1”, the “1” implying it’s the first in a series. I think we would definitely be interested in competing next year!