CN111581534A - Rumor propagation tree structure optimization method based on consistency of vertical place - Google Patents
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Abstract
The invention belongs to the technical field of authenticity of social media information release, and particularly relates to a rumor propagation tree structure optimization method based on the consistency of the legislation. Aiming at the scene of rumor detection of a top-down recursion neural network model based on a tree structure in social media, the invention enables the nodes with the same vertical field in the rumor propagation tree structure to be combined into a super node, strengthens the vertical field representation of the node to the father node of the node, optimizes the propagation tree structure and improves the rumor detection performance. The invention can greatly reduce the number of branches and nodes in the propagation tree, thereby reducing the computation time complexity of rumor detection and realizing optimization on the performance of rumor detection.
Description
Technical Field
The invention belongs to the technical field of authenticity of social media information release, and particularly relates to a rumor propagation tree structure optimization method based on the consistency of the legislation.
Background
Rumors are generally defined as information that appears and spreads between persons whose true facies value has not been proven or intentionally faked. In recent years, the popularity of social media over Twitter, Facebook, etc. has further facilitated the spread of rumors by enabling unreliable sources to spread large amounts of unverified information among people. Therefore, real-time tracking and uncovering rumors becomes especially important. However, the conventional feature-based method extracts features from the statistics of the spurious message, the author of the message, and their responses, and forms a flat feature vector for rumor detection. However, the method ignores the propagation structure of the message, so that the existing method has high complexity of computation time and low efficiency. Therefore, studies on rumor propagation structural features are receiving increasing attention from researchers.
Wu et al propose a hybrid SVM classifier that deeply combines radial basis function RBF with a random walk based graph kernel to detect rumors on the green microblog by capture plane and propagation mode. Ma et al captured the similarity of propagation trees by computing their similar substructures using the tree kernel to identify different types of rumors on Twitter. Ma et al also propose a recurrent neural network model based on bottom-up and top-down tree structures for representation learning and classification of rumors. However, the prior rumor detection research based on rumor propagation structural features has not considered the problem of optimizing on the propagation structure of the message. In the rumor propagation mode tree graph, the propagation tree structure optimization method based on the consistency of the position utilizes each node to classify the position of the parent node table of the node, and the nodes with the same position can implement the propagation tree optimization strategy to form a super node, thereby simplifying the structure of the propagation tree.
Disclosure of Invention
The invention aims to provide a rumor propagation tree structure optimization method based on position consistency, which is applied to a social media rumor detection scene.
The purpose of the invention is realized by the following technical scheme: the method comprises the following steps:
step 1: inputting a rumor propagation tree diagram G;
step 2: traversing each node in the rumor propagation tree graph G based on breadth first of consistency in the vertical place to obtain the optimized rumor propagation tree graph G1；
Step 2.1: selecting a node in the rumor propagation tree graph G, and judging whether the node has a child node or not; if the node has a child node, executing the step 2.2; if the node has no child node, ending the operation on the node, and executing the step 2.4;
step 2.2: traversing each child node of the node, and acquiring the position of each child node;
step 2.3: judging whether the child nodes have the same position or not; if the child nodes have the same position, combining the child nodes with the same position into a super node; if the child nodes do not have the same position, ending the operation on the node, and executing the step 2.4;
step 2.4: judging whether the traversal of all nodes in the rumor propagation tree graph G is finished or not; if the traversal of all nodes in the rumor propagation tree graph G is completed, outputting the optimized propagation tree graph G1(ii) a Otherwise, returning to the step 2.1;
and step 3: depth-first traversal propagation tree graph G based on consistency of vertical positions1Obtaining the optimized propagation tree graph G by each node in the tree2；
Step 3.1: selecting optimized rumor propagation tree graph G1And judging whether the node has a child node. If the node has a child node, executing the step 3.2; if the node has no child node, ending the operation on the node, and executing the step 3.5;
step 3.2: selecting a child node of the node, and judging whether the child node has a next-level child node; if the child node has a next-level child node, executing the step 3.3; if the child node does not have a next level child node, executing step 3.4;
step 3.3: checking whether the position of the next-level child node of the child node is supported; if the position of the child node at the next level of the child node is the support, combining the child node and the child node at the next level into a super node; otherwise, executing step 3.4;
step 3.4: judging whether the traversal of all child nodes of the node is finished or not; if the traversal of all the child nodes of the node is finished, executing the step 3.5; otherwise, returning to the step 3.2;
step 3.5: judging whether the optimized rumor spreading tree diagram G is finished or not1Traversing all nodes in the tree; if the optimized rumor propagation tree diagram G is completed1The final optimized propagation tree graph G is output after the traversal of all the nodes in the tree graph2(ii) a Otherwise, returning to the step 3.1.
The invention has the beneficial effects that:
the invention provides a rumor propagation tree structure optimization method based on the consistency of the vertical field aiming at the scene of rumor detection of a top-down recursion neural network model based on a tree structure in social media. The invention can greatly reduce the number of branches and nodes in the propagation tree, thereby reducing the computation time complexity of rumor detection and realizing optimization on the performance of rumor detection.
Drawings
Fig. 1 is a diagram of a rumor propagation tree in social media.
Fig. 2 is a schematic diagram of rumor propagation in social media optimized based on a place-consistent breadth-first propagation tree structure.
Fig. 3 is a schematic diagram of rumor propagation in social media optimized based on a position-consistent depth-first propagation tree structure.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
By taking fig. 1 as an example, the propagation tree optimization problem based on breadth and depth priority with consistent ground is introduced in sequence. Fig. 1 is a tree diagram of rumor propagation structure in a part of original social media, and fig. 2 and 3 are tree diagrams of rumor propagation structure of social media after being applied to propagation tree optimization methods based on breadth-first and depth-first, respectively. The invention aims to provide a propagation tree optimization algorithm based on consistency of a place under a social media rumor detection scene, which comprises a propagation tree optimization method based on breadth-first and a propagation tree optimization method based on depth-first, and can greatly reduce the number of branches and nodes in a propagation tree, thereby reducing the computation time complexity of rumor detection and realizing optimization on the performance of the rumor detection.
The purpose of the invention is realized as follows:
1. inputting a rumor propagation tree diagram G;
2. traversing each node in the propagation tree graph G in a priority mode based on the breadth with consistent ground to obtain the optimized propagation tree graph G1；
3. Depth-first traversal propagation tree graph G based on consistency of vertical positions1Obtaining the optimized propagation tree graph G by each node in the tree2；
4. Returning to the finally optimized propagation tree graph G2I.e. graph G'.
The invention provides a rumor propagation tree structure optimization method based on the consistency of the vertical field aiming at the scene of rumor detection of a top-down recursion neural network model based on a tree structure in social media. The invention can greatly reduce the number of branches and nodes in the propagation tree, thereby reducing the computation time complexity of rumor detection and realizing optimization on the performance of rumor detection.
1. The invention relates to a directed graph with emotion labels: top-down rumor propagation tree plots. It naturally follows the direction of propagation of rumor information, and defines a rumor detection dataset as a set of statements C ═ C{C1,C2,…,C|c|A, wherein each declaration CiCorresponding to a source tweet, the tweet ideally consists of all the relevant response tweets arranged in time sequence. Thus, the propagation tree is denoted t (r) ═ r<Vi,Ei>In which V isi＝CiComposed of all related posts as nodes, and EiRepresents a set of directed links where u → v represents information that flows from u to v, which sees it and provides a response to u, i.e., an emotion label on a directed edge.
2. Breadth-first traversal of each node V in the graphi: (1) and judging whether the node has a child node or not. If not, then based on
Finishing the spread tree optimization algorithm with consistent vertical place and breadth first; (2) traversing each child node of the node and obtaining the position of each child node, such as support, denial or question; (3) and judging whether the child nodes have the same position or not. If not, the propagation tree optimization algorithm based on the breadth-first consistent in the vertical place is ended; (4) and executing a breadth-first propagation tree optimization method based on consistent position. As shown in fig. 1, when traversing to node 2, it is found that nodes 6 and 7 in its child nodes are identical to each other in their positions, and whether both nodes recognize them, then we can use our proposed optimization strategy based on the breadth-first propagation tree with position consistency to merge the child nodes with position consistency into a super node, so as to simplify the structure of the propagation tree. Fig. 2 is a simplified rumor propagation tree diagram.
3. Each node V in the depth-first traversal graphi: (1) and judging whether the node has a child node or not. If not, finishing the depth-first propagation tree optimization algorithm based on the consistency of the position; (2) and traversing each child node of the node, and judging whether the child node has the child node. If not, finishing the propagation tree optimization algorithm based on depth priority; (3) see if their position is supported. If not, finishing the propagation tree optimization algorithm based on depth priority; if yes, the child node and the next level child node are merged into a super node (4), then the child node of the node is traversed, and whether the child node has children or not is judgedThe child nodes check whether the positions of the child nodes are supported; (5) circularly executing the step (4) until a certain node has no child node or all the child nodes of the certain node are not supported from the standpoint; (6) and executing a depth-first propagation tree optimization strategy based on position consistency. As shown in fig. 2, when traversing to a super node, it is found that the node 9 supports the position of its parent node, and the node 11 supports the position of its parent node 9, so we can find that the positions of the super node, the node 9 and the node 11 are consistent, that is, the positions of the super node, the node 9 and the node 11 are the same and are all considered, so we can implement a depth-first propagation tree optimization strategy based on the consistency of the positions. Fig. 3 is a diagram of an optimized rumor propagation tree structure.
4. Thus, an optimized structure tree diagram of the rumor propagation tree is generated.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A rumor propagation tree structure optimization method based on position consistency is characterized by comprising the following steps:
step 1: inputting a rumor propagation tree diagram G;
step 2: traversing each node in the rumor propagation tree graph G based on breadth first of consistency in the vertical place to obtain the optimized rumor propagation tree graph G1；
Step 2.1: selecting a node in the rumor propagation tree graph G, and judging whether the node has a child node or not; if the node has a child node, executing the step 2.2; if the node has no child node, ending the operation on the node, and executing the step 2.4;
step 2.2: traversing each child node of the node, and acquiring the position of each child node;
step 2.3: judging whether the child nodes have the same position or not; if the child nodes have the same position, combining the child nodes with the same position into a super node; if the child nodes do not have the same position, ending the operation on the node, and executing the step 2.4;
step 2.4: judging whether the traversal of all nodes in the rumor propagation tree graph G is finished or not; if the traversal of all nodes in the rumor propagation tree graph G is completed, outputting the optimized propagation tree graph G1(ii) a Otherwise, returning to the step 2.1;
and step 3: depth-first traversal propagation tree graph G based on consistency of vertical positions1Obtaining the optimized propagation tree graph G by each node in the tree2；
Step 3.1: selecting optimized rumor propagation tree graph G1And judging whether the node has a child node. If the node has a child node, executing the step 3.2; if the node has no child node, ending the operation on the node, and executing the step 3.5;
step 3.2: selecting a child node of the node, and judging whether the child node has a next-level child node; if the child node has a next-level child node, executing the step 3.3; if the child node does not have a next level child node, executing step 3.4;
step 3.3: checking whether the position of the next-level child node of the child node is supported; if the position of the child node at the next level of the child node is the support, combining the child node and the child node at the next level into a super node; otherwise, executing step 3.4;
step 3.4: judging whether the traversal of all child nodes of the node is finished or not; if the traversal of all the child nodes of the node is finished, executing the step 3.5; otherwise, returning to the step 3.2;
step 3.5: judging whether the optimized rumor spreading tree diagram G is finished or not1Traversing all nodes in the tree; if the optimized rumor propagation tree diagram G is completed1The final optimized propagation tree graph G is output after the traversal of all the nodes in the tree graph2(ii) a Otherwise, returning to the step 3.1.
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