You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


---
abstract: 'We present FAKTA which is a unified framework that integrates various components of a fact checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level to explain its predictions.'
author:
- |
    Moin Nadeem, Wei Fang, Brian Xu, Mitra Mohtarami, James Glass\
    MIT Computer Science and Artificial Intelligence Laboratory\
    Cambridge, MA, USA\
    [{mnadeem, weifang, bwxu, mitram, <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>]{}\
bibliography:
- 'naaclhlt2019.bib'
title: 'FAKTA: An Automatic End-to-End Fact Checking System'
---

Introduction
============

With the rapid increase of fake news in social media and its negative influence on people and public opinion [@mihaylov-georgiev-nakov:2015:CoNLL; @ACL2016:trolls; @Vosoughi1146], various organizations are now performing [*manual*]{} fact checking on suspicious claims. However, manual fact-checking is a time consuming and challenging process. As an alternative, researchers are investigating [*automatic*]{} fact checking which is a multi-step process and involves: (*i*) retrieving potentially relevant documents for a given claim [@AAAIFactChecking2018; @R17-1046], (*ii*) checking the reliability of the media sources from which documents are retrieved, (*iii*) predicting the stance of each document with respect to the claim [@mitra2018memory; @brian:nips2018], and finally (*iv*) predicting factuality of given claims [@AAAIFactChecking2018]. While previous works separately investigated individual components of the fact checking process, in this work, we present a unified framework titled FAKTA that integrates these components to not only predict the factuality of given claims, but also provide evidence at the document and sentence level to explain its predictions. To the best of our knowledge, FAKTA is the only system that offers such a capability.

FAKTA
=====

![image](FAKTA_model.png){width="145mm"}

Figure \[fig:FAKTA-architecture\] illustrates the general architecture of FAKTA. The system is accessible via a Web browser and has two sides: client and server. When a user at the client side submits a textual claim for fact checking, the server handles the request by first passing it into the document retrieval component to retrieve a list of top-K relevant documents (see Section \[Document-Retrieval\]) from four types of sources: Wikipedia, highly-reliable, mixed reliability and low reliability mainstream media (see Section \[Sources\]). The retrieved documents are passed to the re-ranking model to refine the retrieval result (see Section \[Document-Retrieval\]). Then, the stance detection component detects the stance/perspective of each relevant document with respect to the claim, typically modeled using labels such as *agree*, *disagree* and *discuss*. This component further provides rationales at the sentence level for explaining model predictions (see Section \[Stance-Detection\]). Each document is also passed to the linguistic analysis component to analyze the language of the document using different linguistic lexicons (see Section \[Linguistic-Analysis\]). Finally, the aggregation component combines the predictions of stance detection for all the relevant documents and makes a final decision about the factuality of the claim (see Section \[Aggregation\]). We describe the components below.

Document Retrieval & Re-ranking Model {#Document-Retrieval}
-------------------------------------

We first convert an input claim to a query by only considering its verbs, nouns and adjectives [@PotthastHGTKRSS13]. Furthermore, claims often contain named entities (e.g., names of persons and organizations). We use the NLTK package to identify named entities in claims, and augment the initial query with all named entities from the claim’s text. Ultimately, we generate queries of $5$–$10$ tokens, which we execute against a search engine. If the search engine does not retrieve any results for the query, we iteratively relax the query by dropping the final tokens one at a time. We also use Apache Lucene[^1] to index and retrieve relevant documents from the $2017$ Wikipedia dump (see our experiments in Section \[sec:results\]). Furthermore, we use the Google API[^2] to search across three pre-defined lists of media sources based on their factuality and reliability as explained in Section \[Sources\]. Finally, the re-ranking model of @D18-1143 ([-@D18-1143]) is applied to select the top-K relevant documents. This model uses all the POS tags in a claim that carry high discriminating power (NN, NNS, NNP, NNPS, JJ, CD) as keywords. The re-ranking model is defined as follows: $$f_{rank}=\frac{|match|}{|claim|} \times \frac{|match|}{|title|} \times score_{init},$$ where $|claim|$, $|title|$, and $|match|$ are the counts of such POS tags in the claim, title of a document, both claim and title respectively, and $score_{init}$ is the initial ranking score computed by Lucene or ranking from Google API.

Sources {#Sources}
-------

While current search engines (e.g., Google, Bing, Yahoo) retrieve relevant documents for a given query from any media source, we retrieve relevant documents from four types of sources: Wikipedia, and high, mixed and low factual media. Journalists often spend considerable time verifying the reliability of their information sources [@Popat:2017:TLE:3041021.3055133; @NguyenKLW18], and some fact-checking organizations have been producing lists of unreliable online news sources specified by their journalists. FAKTA utilizes information about news media listed on the Media Bias/Fact Check (MBFC) website[^3], which contains manual annotations and analysis of the factuality of $2,500$ news websites. Our list from MBFC includes  $1,300$ websites annotated by journalists as *high* or *very high*,  $700$ websites annotated as *low* and *low-questionable*, and  $500$ websites annotated as *mixed* (i.e., containing both factually true and false information). Our document retrieval component retrieves documents from these three types of media sources (i.e., *high*, *mixed* and *low*) along with Wikipedia that mostly contains factually-true information.

![image](demo.png){width="141mm"}

Stance Detection & Evidence Extraction {#Stance-Detection}
--------------------------------------

In this work, we use our best model presented in [@brian:nips2018] for stance detection. To the best of our knowledge, this model is the current state-of-the-art system on the Fake News Challenge (FNC) dataset.[^4] Our model combines Bag of Words (BOW) and Convolutional Neural Networks (CNNs) in a two-level *hierarchy* scheme, where the first level predicts whether the label is *related* or *unrelated* (see Figure \[fig:screenshot\], the top-left pie chart in FAKTA), and then related documents are passed to the second level to determine their stances, *agree*, *disagree*, and *discuss* labels (see Figure \[fig:screenshot\], the bottom-left pie chart in FAKTA). Our model is further supplemented with an adversarial domain adaptation technique which helps it overcome the limited size of labeled data when training through different domains. To provide rationales for model prediction, FAKTA further processes each sentence in the document with respect to the claim and computes a stance score for each sentence. The relevant sentences in the document are then highlighted and color coded with respect to stance labels (see Figure \[fig:screenshot\]). FAKTA provides the option for re-ordering these rationales according to a specific stance label.

Linguistic Analysis {#Linguistic-Analysis}
-------------------

We analyze the language used in documents using the following linguistic markers: —*Subjectivity lexicon* [@Riloff:2003:LEP:1119355.1119369]: which contains weak and strong subjective terms (we only consider the strong subjectivity cues),

—*Sentiment cues* [@Liu:2005:OOA:1060745.1060797]: which contains *positive* and *negative* sentiment cues, and

—*Wiki-bias lexicon* [@Recasens:ACL:13]: which involves bias cues and controversial words (e.g., [*abortion*]{} and [*execute*]{}) extracted from the Neutral Point of View Wikipedia corpus [@Recasens:ACL:13].

Finally, we compute a score for the document using these cues according to Equation , where for each lexicon type $L_i$ and document $D_j$, the frequency of the cues for $L_i$ in $D_j$ is normalized by the total number of words in $D_j$: $$\label{Ling-equation}\footnotesize
L_i(D_j) = \dfrac{\sum\limits_{cue \in L_i} {count(cue, D_j)}}{\sum\limits_{w_k \in D_j} {count(w_k, D_j)}}$$

These scores are shown in a radar chart in Figure \[fig:screenshot\]. Furthermore, FAKTA provides the option to see a lexicon-specific word cloud of frequent words in each documents (see Figure \[fig:screenshot\], the right side of the radar chart which shows the word cloud of Sentiment cues in the document).

Aggregation {#Aggregation}
-----------

Stance Detection and Linguistic Analysis components are executed in parallel against all documents retrieved by our document retrieval component from each type of sources. All the stance scores are averaged across these documents, and the aggregated scores are shown for each [*agree*]{}, [*disagree*]{} and [*discuss*]{} categories at the top of the ranked list of retrieved documents. Higher agree score indicates the claim is factually true, and higher disagree score indicates the claim is factually false.

Evaluation and Results
======================

We use the Fact Extraction and VERification (FEVER) dataset [@FEVER] to evaluate our system. In FEVER, each claim is assigned to its relevant Wikipedia documents with agree/disagree stances to the claim, and claims are labeled as *supported* (SUP, i.e. factually true), *refuted* (REF, i.e. factually false), and *not enough information* (NEI, i.e., there is not any relevant document for the claim in Wikipedia). The data includes a total of  $145$K claims, with around $80$K, $30$K and $35$K SUP, REF and NEI labels respectively. *Document Retrieval:* Table \[tab:Retrieval-results\] shows results for document retrieval. We use various search and ranking algorithms that measure the similarity between each input claim as query and Web documents. Lines 1–11 in the table show the results when we use Lucene to index and search the data corpus with the following retrieval models: BM25 [@Robertson96okapiat] (Line 1), Classic based on the TF.IDF model (Line 2), and Divergence from Independence (DFI) [@Kocabas:2014:NTW:2592838.2592866] (Line 3). We also use Divergence from Independence Randomness (DFR) [@Amati:2002:PMI:582415.582416] with different term frequency normalization, such as the normalization provided by Dirichlet prior (DFR$_{H_3}$) (Line 4) or a Zipfian relation prior (DFR$_{z}$) (Line 5). We also consider Information Based (IB) models [@Clinchant:2010:IMA:1835449.1835490] with Log-logistic (IB$_{LL}$) (Line 6) or Smoothed power-law (IB$_{SPL}$) (Line 7) distributions. Finally, we consider LMDirichlet [@Zhai:2001:SSM:383952.384019] (Line 8), and LMJelinek [@Zhai:2001:SSM:383952.384019] with different settings for its hyperparameter (Lines 9–11). According to the resulting performance at different ranks {1–20}, we select the ranking algorithm DFR$_{z}$ (Lucene$_{DFR_{Z}}$) as our retrieval model.

In addition, Lines 12–13 show the results when claims are converted to queries as explained in Section \[Document-Retrieval\]. The results (Lines 5 and 12) show that Lucene performance decreases with query generation. This might be because the resulting queries become more abstract than their corresponding claims which may introduce some noise to the intended meaning of claims. However, Lines 14–15 show that our re-ranking model, explained in Section \[Document-Retrieval\], can improve both Lucene and Google results.

*FAKTA Full Pipeline:* The complete pipeline consists of document retrieval and re-ranking model (Section \[Document-Retrieval\]), stance detection and rationale extraction[^5] (Section \[Stance-Detection\]) and aggregation model (Section \[Aggregation\]). Table \[tab:FAKTA-results\] shows the results for the full pipeline. Lines 1–3 show the results for all three SUP, REF, and NEI labels (3lbl) and Randomly Sampled (RS) documents from Wikipedia for the NEI label. We label claims as NEI if the most relevant document retrieved has a retrieval score less than a threshold, which was determined by tuning on development data. Line 1 is the multi-layer perceptron (MLP) model presented in [@DBLP:journals/corr/RiedelASR17]. Lines 2–3 are the results for our system when using Lucene (L) and Google API (G) for document retrieval. The results show that our system achieves the highest performance on both F$_{1(Macro)}$ and accuracy (Acc) using Google as retrieval engine. We repeat our experiments when considering only SUP and REF labels (2lbl) and the results are significantly higher than the results with 3lbl (Lines 4–5).

\[sec:results\]

The System in Action
====================

The current version of FAKTA[^6] and its short introduction video[^7] and source code[^8] are available online. FAKTA consists of three views:

—*The text entry view*: to enter a claim to be checked for factuality.

—*Overall result view*: includes four lists of retrieved documents from four factuality types of sources: Wikipedia, and high-, mixed-, and low-factuality media (Section \[Sources\]). For each list, the final factuality score for the input claim is shown at the top of the page (Section \[Aggregation\]), and the stance detection score for each document appears beside it.

—*Document result view*: when selecting a retrieved document, FAKTA shows the text of the document and highlights its important sentences according to their stance scores with respect to the claim. The stance detection results for the document are further shown as pie chart at the left side of the view (Section \[Stance-Detection\]), and the linguistic analysis is shown at the bottom of the view (Section \[Linguistic-Analysis\]).

Related Work
============

Automatic fact checking [@brian:nips2018] centers on evidence extraction for given claims, reliability evaluation of media sources [@D18-1389], stance detection of documents with respect to claims [@mitra2018memory; @brian:nips2018; @baly2018integrating], and fact checking of claims [@AAAIFactChecking2018]. These steps correspond to different Natural Language Processing (NLP) and Information Retrieval (IR) tasks including information extraction and question answering [@shiralkar2017finding]. Veracity inference has been mostly approached as text classification problem and mainly tackled by developing linguistic, stylistic, and semantic features [@rashkin2017truth; @AAAIFactChecking2018; @nakov2017do], as well as using information from *external* sources [@AAAIFactChecking2018; @R17-1046].

These steps are typically handled in isolation. For example, previous works [@liar-P17-2067; @Nicole:nips2018] proposed algorithms to predict factuality of claims by mainly focusing on only input claims (i.e., step (*iv*) and their metadata information (e.g., the speaker of the claim). In addition, recent works on the Fact Extraction and VERification (FEVER) [@FEVER] has focused on a specific domain (e.g., Wikipedia).

To the best of our knowledge, there is currently no end-to-end systems for fact checking which can search through Wikipedia and mainstream media sources across the Web to fact check given claims. To address these gaps, our FAKTA system covers all fact-checking steps and can search across different sources, predict the factuality of claims, and present a set of evidence to explain its prediction.

Conclusion
==========

We have presented FAKTA–an online system for automatic end-to-end fact checking of claims. FAKTA can assist individuals and professional fact-checkers to check the factuality of claims by presenting relevant documents and rationales as evidence for its predictions. In future work, we plan to improve FAKTA’s underlying components (e.g., stance detection), extend FAKTA to cross-lingual settings, and incorporate temporal information for fact checking.

Acknowledgments {#acknowledgments .unnumbered}
===============

We thank anonymous reviewers for their insightful comments, suggestions, and feedback. This research was supported in part by HBKU Qatar Computing Research Institute (QCRI), DSTA of Singapore, and Intel AI.

[^1]: <https://lucene.apache.org>

[^2]: <https://developers.google.com/custom-search>

[^3]: <https://mediabiasfactcheck.com>

[^4]: <http://www.fakenewschallenge.org>

[^5]: We used Intel AI’s Distiller [@neta_zmora_2018_1297430] to compress the model.

[^6]: <http://fakta.mit.edu>

[^7]: <http://fakta.mit.edu/video>

[^8]: <https://github.com/moinnadeem/fakta>