# Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

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## Abstract

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## 1. Introduction

- Conducting different feature extraction models (i.e., TF, BTF, and TF-IDF) and eight well-known ML algorithms to identify suitable models for detecting fake news in Arabic tweets.
- Adapting a binary variant of the HHO algorithm, for the first time in the text classification domain, with the sake of eliminating the irrelevant/redundant features and enhancing the performance of classification models. HHO has been recently utilized as an FS approach to deal with complex datasets by the first author of this work and his co-authors in [22,23]. HHO has shown its superiority compared to several recent optimization algorithms such as Binary Gray Wolf Optimization (BGWO) and Binary WOA in the FS domain. For this reason, it has been nominated in this work to tackle the problem of FS in Arabic text classification.
- The efficiency of the proposed model has been verified by comparing it with other state-of-the-art approaches and has shown promising results.

## 2. Review of Related Works

#### 2.1. Sentiment Analysis

#### 2.2. False Information Detection

## 3. Theoretical Background (Preliminaries)

#### 3.1. Natural Language Processing (NLP)

#### 3.1.1. Tokenization and Linguistic Modules

#### 3.1.2. Text Vectorization

#### 3.1.3. Term Frequency-Based Model

#### 3.1.4. Term Frequency-Inverse Document Frequency Scheme

#### 3.2. Harris Hawks Optimizer (HHO)

#### 3.2.1. Exploration Phase

#### 3.2.2. Shifting from Exploration to Exploitation

#### 3.2.3. Exploitation Phase

**Soft besiege:**This phase simulates the behavior of the rabbit when it still has energy and tries to escape ($r\ge 0.5$ and $\left|E\right|\ge 0.5$). During these attempts, the hawks try to encircle the prey softly. This behavior is modeled by Equations (8) and (9):$$X(t+1)=\mathsf{\Delta}X\left(t\right)-E\left|J{X}_{prey}\left(t\right)-X\left(t\right)\right|$$$$\mathsf{\Delta}X\left(t\right)={X}_{prey}\left(t\right)-X\left(t\right)$$**Hard besiege:**The next level of the encircling process will be performed when the rabbit is extremely exhausted, and its escaping energy is low ($r\ge 0.5$ and $\left|E\right|<0.5$). Accordingly, the hawks encircle the intended prey hardly. This behavior is modeled by Equation (10):$$X(t+1)={X}_{prey}\left(t\right)-E\left|\mathsf{\Delta}X\left(t\right)\right|$$**Soft besiege with progressive rapid dives:**When the prey still has sufficient energy to escape ($\left|E\right|\ge 0.5$) but ($r<0.5$), hawks can decide (evaluate) their next action based on the rule represented in Equation (11):$$Y={X}_{prey}\left(t\right)-E\left|J{X}_{prey}\left(t\right)-X\left(t\right)\right|$$

**Hard besiege with progressive rapid dives:**In the case of ($r<0.5$ and $\left|E\right|<0.5$), the hawks perform a hard besiege to approach the prey. They try to decrease the distances between their average location and the targeted prey. Accordingly, the rule given in Equation (16) is performed:$$X(t+1)=\left\{\begin{array}{cc}Y& ifF\left(Y\right)<F\left(X\right(t\left)\right)\\ Z& ifF\left(Z\right)<F\left(X\right(t\left)\right)\end{array}\right.$$$$Y={X}_{prey}\left(t\right)-E\left|J{X}_{prey}\left(t\right)-{X}_{avg}\left(t\right)\right|$$$$Z=Y+S\times LF\left(D\right)$$

## 4. The Proposed Methodology

#### 4.1. Collection of the Tweets Dataset

#### 4.2. Data Preprocessing

#### 4.3. Feature Extraction

- User-profile features only.
- Contend-based features only.
- Word features only including either TF, TF-IDF, or BoW.
- User-profile and content-based features
- User-profile features and one set of word features (TF, TF-IDF, BoW).
- Content-based features and one set of word features (TF, TF-IDF, BoW).
- User-profile features and content-based features and one set of word features (TF, TF-IDF, BoW).

#### 4.4. Selection of Classification Algorithms

#### 4.5. Feature Selection Using Binary HHO

#### 4.6. Performance Evaluation Measures

- Accuracy: It is the number of correctly classified real and fake tweets divided by the total number of predictions. Accuracy is calculated using Equation (22):$$Accuracy=\frac{TP+TN}{TP+TN+FP+FN}$$
- Recall: It is known as true positive rate, which is the ratio of correctly recognized tweets as positive (real) over the total number of actual positives, calculated as in Equation (23):$$Recall=\frac{TP}{TP+FN}$$
- Precision: It is known as a positive predictive value, which is the fraction of tweets that are correctly recognized as positive (real) among the total number of positive predictions, calculated as in Equation (24):$$Precision=\frac{TP}{TP+FP}$$
- F1_measure: It is defined as a harmonic mean of recall and precision metrics and can be computed as given in Equation (25). It is known as a balanced F-score since recall and precision are evenly weighted. This measure is informative in terms of incorrectly classified instances and useful to balance the prediction performances on the fake and real classes:$$F1-measure=2.\frac{recall.precision}{recall+precision}$$

## 5. Results and Discussion

#### 5.1. Experimental Setup

**boldface**format.

#### 5.2. Evaluation of Classification Methods

#### 5.3. Assessing the Impact of n-Grams

#### 5.4. Analysis of BHHO-Based Feature Selection

#### 5.5. Comparison with Results of the Literature

## 6. Conclusions and Future Perspectives

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Behavior of E during two runs and 500 iterations [20].

Symbol | Description |
---|---|

X | Position vector of hawks (search agents) |

${X}_{rand}$ | Position of randomly selected agent |

${X}_{prey}$ | Position vector of best agent so far (rabbit) |

${X}_{avg}$ | Average position of all agents |

${r}_{1}$, ${r}_{2}$, ${r}_{3}$, ${r}_{4}$, ${r}_{5}$, q | uniformly random numbers within [0,1] |

t, T, N | current iteration, number of iterations, number of search agents |

$LB$, $UB$ | Lower and upper bounds of decision variables |

D | dimension |

J | Random jump strength of the prey during the fleeing procedure |

S | Random vector by size 1 × D |

${E}_{0}$, E | Initial value of escaping energy, escaping energy |

Query Topic | No. of Tweets | Credible (%) | Non-Credible (%) | Undecidable (%) |
---|---|---|---|---|

The forces of the Syrian government | 1791 | 63.1% | 28.5% | 8.4% |

Syrian revolution | 1232 | 35.6% | 51.0% | 13.4% |

**Table 3.**List of previously extracted features [31].

User-Profile Features | Content-Based Features | |||
---|---|---|---|---|

avg_hashtags | avg_URLs | has_mention | mentions_count | URL_shortner |

followers_to_friends | avg_retweet | retweets_num | is_retweet | is_reply |

followers_count | tweet_time_spacing | retweeted | day_of_week | #_words |

focused_topic | default_image | length_chars | has_URL | URLs_count |

has_desc | desc_len | has_hashtag | hashtags_count | #_unique_words |

username_len | avg_tweet_len | # unique chars | has_? | #_? |

listed_count | status_count | has_! | #_! | #_ellipses |

retweet_fraction | friends_to_follower | #_symbols | has_pos_sent | has_neg_sent |

is_verified | registration_diff | pos_score | neg_score |

Name | Transfer Function Formula Function |
---|---|

V1 | $T\left(x\right)=|\mathrm{erf}\left(\frac{\sqrt{\mathsf{\Pi}}}{2}x\right)|$ |

V2 | $T\left(x\right)=|tanh(x\left)\right|$ |

V3 | $T\left(x\right)=|\left(x\right)/\sqrt{1+{x}^{2}}|$ |

V4 | $T\left(x\right)=|\frac{2}{\mathsf{\Pi}}\mathrm{arc}\phantom{\rule{4.pt}{0ex}}\mathrm{tan}\left(\frac{\mathsf{\Pi}}{2}x\right)|$ |

Predicted Class | |||
---|---|---|---|

positive | negative | ||

Actual class | positive | TP | FN |

negative | FP | TN |

Common Parameters | ||
---|---|---|

population size | 20 | |

Number of iterations | 100 | |

Number of runs | 10 | |

Dimension | #features | |

K for cross validation | 5 | |

Fitness function | alpha = 0.99, Beta = 0.01 | |

Internal Parameters | ||

Algorithm | parameter | value |

BHHO | Convergence constant E | [2 0] |

BWOA | convergence constant a | [2 0] |

Spiral factor b | 1 | |

BBA | ${Q}_{min}$, ${Q}_{max}$ | 0, 2 |

loudness A | 0.5 | |

Pulse rate r | 0.5 | |

BGWO | convergence constant a | [2 0] |

BFFA | $\alpha $ | 0.5 |

$\beta $ | 0.2 | |

$\gamma $ | 1 | |

BMFO | Convergence constant a | [−1 −2] |

Spiral factor b | 1 | |

GA | crossover probability | 0.9 |

mutation probability | 0.01 | |

crossover type | single point | |

selection strategy | roulette wheel | |

elite | 2 | |

BCS | Discovery rate of alien solutions ${p}_{a}$ | 0.25 |

**Table 7.**Classification performance of classifiers over generated data using different vectorization methods.

Classifier | Vectorizer | Accuracy | Recall | Precision | F1_Score | Rank |
---|---|---|---|---|---|---|

RF | TF | 0.7539 | 0.8240 | 0.7603 | 0.7909 | 8.63 |

TF-IDF | 0.7598 | 0.8059 | 0.7771 | 0.7912 | 10.25 | |

BTF | 0.7480 | 0.8211 | 0.7544 | 0.7863 | 10.50 | |

SVM | TF | 0.7297 | 0.8202 | 0.7330 | 0.7741 | 14.75 |

TF-IDF | 0.7539 | 0.8497 | 0.7485 | 0.7959 | 8.13 | |

BTF | 0.7652 | 0.8145 | 0.7796 | 0.7966 | 8.50 | |

LR | TF | 0.7695 | 0.8192 | 0.7827 | 0.8006 | 7.00 |

TF-IDF | 0.7802 | 0.8392 | 0.7861 | 0.8118 | 2.63 | |

BTF | 0.7802 | 0.8173 | 0.7983 | 0.8077 | 4.63 | |

DT | TF | 0.7152 | 0.7507 | 0.7465 | 0.7486 | 17.75 |

TF-IDF | 0.7163 | 0.7364 | 0.7551 | 0.7457 | 16.75 | |

BTF | 0.6980 | 0.7279 | 0.7349 | 0.7314 | 19.75 | |

KNN | TF | 0.6964 | 0.8221 | 0.6957 | 0.7536 | 16.25 |

TF-IDF | 0.7055 | 0.8382 | 0.6998 | 0.7628 | 14.75 | |

BTF | 0.7077 | 0.9001 | 0.6830 | 0.7767 | 13.75 | |

LDA | TF | 0.6319 | 0.6584 | 0.6798 | 0.6689 | 23.00 |

TF-IDF | 0.6400 | 0.6803 | 0.6816 | 0.6810 | 22.00 | |

BTF | 0.6169 | 0.6489 | 0.6647 | 0.6567 | 24.00 | |

NB | TF | 0.7415 | 0.8211 | 0.7465 | 0.7821 | 12.38 |

TF-IDF | 0.7474 | 0.8049 | 0.7615 | 0.7826 | 12.50 | |

BTF | 0.7415 | 0.8211 | 0.7465 | 0.7821 | 12.38 | |

XGboost | TF | 0.7781 | 0.8069 | 0.8015 | 0.8042 | 5.50 |

TF-IDF | 0.7727 | 0.7992 | 0.7985 | 0.7989 | 7.75 | |

BTF | 0.7770 | 0.8097 | 0.7983 | 0.8040 | 6.50 |

Word Features | User-Related | Content-Related | Classifier | Accuracy | Recall | Precision | F1_Score |
---|---|---|---|---|---|---|---|

√ | √ | √ | RF | 0.7598 | 0.8059 | 0.7771 | 0.7912 |

SVM | 0.7539 | 0.8497 | 0.7485 | 0.7959 | |||

LR | 0.7802 | 0.8392 | 0.7861 | 0.8118 | |||

DT | 0.7163 | 0.7364 | 0.7551 | 0.7457 | |||

KNN | 0.7055 | 0.8382 | 0.6998 | 0.7628 | |||

LDA | 0.6400 | 0.6803 | 0.6816 | 0.6810 | |||

NB | 0.7474 | 0.8049 | 0.7615 | 0.7826 | |||

XGboost | 0.7727 | 0.7992 | 0.7985 | 0.7989 | |||

╳ | √ | √ | RF | 0.7238 | 0.7545 | 0.7560 | 0.7552 |

SVM | 0.7093 | 0.8639 | 0.6953 | 0.7705 | |||

LR | 0.7082 | 0.8192 | 0.7092 | 0.7603 | |||

DT | 0.6711 | 0.7012 | 0.7121 | 0.7066 | |||

KNN | 0.6722 | 0.7688 | 0.6877 | 0.7260 | |||

LDA | 0.7131 | 0.8145 | 0.7163 | 0.7622 | |||

NB | 0.5287 | 0.2969 | 0.6933 | 0.4157 | |||

XGboost | 0.7303 | 0.7764 | 0.7535 | 0.7648 | |||

√ | ╳ | ╳ | RF | 0.7534 | 0.7774 | 0.7841 | 0.7807 |

SVM | 0.7636 | 0.7840 | 0.7946 | 0.7893 | |||

LR | 0.7571 | 0.7926 | 0.7807 | 0.7866 | |||

DT | 0.7045 | 0.7412 | 0.7370 | 0.7391 | |||

KNN | 0.7179 | 0.8934 | 0.6945 | 0.7815 | |||

LDA | 0.6239 | 0.6708 | 0.6657 | 0.6682 | |||

NB | 0.7474 | 0.8049 | 0.7615 | 0.7826 | |||

XGboost | 0.7480 | 0.7631 | 0.7847 | 0.7738 | |||

╳ | √ | ╳ | RF | 0.6889 | 0.7022 | 0.7351 | 0.7182 |

SVM | 0.6577 | 0.8107 | 0.6605 | 0.7279 | |||

LR | 0.6432 | 0.7650 | 0.6585 | 0.7077 | |||

DT | 0.6486 | 0.6832 | 0.6910 | 0.6871 | |||

KNN | 0.6373 | 0.6993 | 0.6718 | 0.6853 | |||

LDA | 0.6464 | 0.7507 | 0.6658 | 0.7057 | |||

NB | 0.5787 | 0.3958 | 0.7363 | 0.5149 | |||

XGboost | 0.6711 | 0.6993 | 0.7129 | 0.7061 | |||

╳ | ╳ | √ | RF | 0.6932 | 0.7878 | 0.7041 | 0.7436 |

SVM | 0.6975 | 0.8839 | 0.6781 | 0.7675 | |||

LR | 0.7012 | 0.8259 | 0.6994 | 0.7574 | |||

DT | 0.6244 | 0.6594 | 0.6702 | 0.6647 | |||

KNN | 0.6760 | 0.7764 | 0.6892 | 0.7302 | |||

LDA | 0.7012 | 0.8316 | 0.6975 | 0.7587 | |||

NB | 0.4718 | 0.1979 | 0.5977 | 0.2974 | |||

XGboost | 0.6814 | 0.7517 | 0.7041 | 0.7271 | |||

√ | √ | ╳ | RF | 0.7614 | 0.7954 | 0.7850 | 0.7902 |

SVM | 0.7603 | 0.7935 | 0.7846 | 0.7890 | |||

LR | 0.7577 | 0.7973 | 0.7788 | 0.7880 | |||

DT | 0.6862 | 0.6927 | 0.7361 | 0.7137 | |||

KNN | 0.7200 | 0.8773 | 0.7017 | 0.7797 | |||

LDA | 0.6271 | 0.6784 | 0.6670 | 0.6726 | |||

NB | 0.7474 | 0.8049 | 0.7615 | 0.7826 | |||

XGboost | 0.7517 | 0.7688 | 0.7868 | 0.7777 | |||

√ | ╳ | √ | RF | 0.7555 | 0.7992 | 0.7749 | 0.7869 |

SVM | 0.7517 | 0.8411 | 0.7498 | 0.7928 | |||

LR | 0.7711 | 0.8344 | 0.7768 | 0.8046 | |||

DT | 0.7109 | 0.7479 | 0.7422 | 0.7450 | |||

KNN | 0.7157 | 0.8649 | 0.7014 | 0.7746 | |||

LDA | 0.6421 | 0.6813 | 0.6839 | 0.6826 | |||

NB | 0.7474 | 0.8049 | 0.7615 | 0.7826 | |||

XGboost | 0.7577 | 0.7812 | 0.7879 | 0.7845 |

Classifier | n-Grams | Accuracy | Recall | Precision | F1_Score | Rank |
---|---|---|---|---|---|---|

LR | 1-gram | 0.7802 | 0.8392 | 0.7861 | 0.8118 | 2.00 |

2-grams | 0.7609 | 0.8335 | 0.7644 | 0.7975 | 5.00 | |

3-grams | 0.7437 | 0.8325 | 0.7440 | 0.7858 | 6.75 | |

1-gram and 2-grams | 0.7749 | 0.8402 | 0.7787 | 0.8082 | 2.50 | |

XGboost | 1-gram | 0.7727 | 0.7992 | 0.7985 | 0.7989 | 3.50 |

2-grams | 0.7689 | 0.7878 | 0.8000 | 0.7939 | 4.75 | |

3-grams | 0.7458 | 0.7697 | 0.7779 | 0.7738 | 7.25 | |

1-gram and 2-grams | 0.7700 | 0.7916 | 0.7992 | 0.7954 | 4.25 |

Method | Measure | Accuracy | Recall | Precision | F1_Score | Rank |
---|---|---|---|---|---|---|

VBHHO1 | Mean | 0.8150 | 0.8552 | 0.8235 | 0.8390 | 1.00 |

Std | 0.0060 | 0.0058 | 0.0098 | 0.0044 | ||

VBHHO2 | Mean | 0.8102 | 0.8540 | 0.8175 | 0.8352 | 2.25 |

Std | 0.0049 | 0.0151 | 0.0065 | 0.0056 | ||

VBHHO3 | Mean | 0.8084 | 0.8441 | 0.8210 | 0.8323 | 2.75 |

Std | 0.0061 | 0.0127 | 0.0079 | 0.0059 | ||

VBHHO4 | Mean | 0.8052 | 0.8432 | 0.8170 | 0.8299 | 4.00 |

Std | 0.0052 | 0.0070 | 0.0061 | 0.0045 |

**boldface**.

Algorithm | Metric | Accuracy | Recall | Precision | F1_Score | Rank |
---|---|---|---|---|---|---|

VBHHO1 | Mean | 0.8150 | 0.8552 | 0.8235 | 0.8390 | 1.00 |

Std | 0.0060 | 0.0058 | 0.0098 | 0.0044 | ||

BWOA | Mean | 0.8045 | 0.8413 | 0.8174 | 0.8290 | 5.75 |

Std | 0.0038 | 0.0163 | 0.0093 | 0.0045 | ||

BMFO | Mean | 0.8052 | 0.8432 | 0.8171 | 0.8298 | 5.13 |

Std | 0.0027 | 0.0157 | 0.0067 | 0.0044 | ||

BGWO | Mean | 0.8073 | 0.8463 | 0.8182 | 0.8319 | 3.50 |

Std | 0.0025 | 0.0118 | 0.0085 | 0.0025 | ||

BGA | Mean | 0.8093 | 0.8457 | 0.8213 | 0.8333 | 2.50 |

Std | 0.0052 | 0.0096 | 0.0080 | 0.0045 | ||

BFFA | Mean | 0.8086 | 0.8432 | 0.8219 | 0.8323 | 3.13 |

Std | 0.0025 | 0.0132 | 0.0059 | 0.0038 | ||

BCS | Mean | 0.7936 | 0.8295 | 0.8091 | 0.8191 | 7.00 |

Std | 0.0056 | 0.0105 | 0.0079 | 0.0050 | ||

BBA | Mean | 0.7902 | 0.8292 | 0.8045 | 0.8166 | 8.00 |

Std | 0.0057 | 0.0094 | 0.0083 | 0.0049 |

**Table 12.**Comparison of the LR classifier before and after applying VBHHO1 feature selection algorithm in terms of classification measures and number of selected features.

Method | No. Features | Accuracy | Recall | Precision | F1_Score |
---|---|---|---|---|---|

LR | 3054 | 78% | 84% | 79% | 81% |

VBHHO1-LR | 984.2 | 82% | 86% | 82% | 84% |

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## Share and Cite

**MDPI and ACS Style**

Thaher, T.; Saheb, M.; Turabieh, H.; Chantar, H.
Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models. *Symmetry* **2021**, *13*, 556.
https://doi.org/10.3390/sym13040556

**AMA Style**

Thaher T, Saheb M, Turabieh H, Chantar H.
Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models. *Symmetry*. 2021; 13(4):556.
https://doi.org/10.3390/sym13040556

**Chicago/Turabian Style**

Thaher, Thaer, Mahmoud Saheb, Hamza Turabieh, and Hamouda Chantar.
2021. "Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models" *Symmetry* 13, no. 4: 556.
https://doi.org/10.3390/sym13040556